NEURAL INSTRUCTIVE SIGNALS IN THE CEREBELLUM

NEURAL INSTRUCTIVE SIGNALS IN THE CEREBELLUM

DISSERTATION SUBMITTED TO THE PROGRAM IN

NEUROSCIENCES AND THE COMMITTEE ON GRADUATE

STUDIES OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

Michael C. Ke May 2010

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/wy417ny4640

© 2010 by Michael Chinwen Ke. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii



http://purl.stanford.edu/wy417ny4640

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Jennifer Raymond, Primary Adviser


John Huguenard


Eric Knudsen


William Newsome

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

iii

iv

Abstract

An understanding of the neural patterns available to guide plasticity in vivo is needed

to bridge our knowledge of synaptic plasticity to its function in learning. I

investigated the patterns of neural activity that trigger plasticity in vivo in a simple

cerebellum-dependent motor learning task, adaptation of the vestibulo-ocular reflex

(VOR), with the specific goal of determining which neurons carry the instructive

signals that trigger plasticity in the circuit for the VOR. The VOR stabilizes images

on the retina during head turns by using vestibular signals to generate compensatory

smooth eye movements in the opposite direction of head motion. Motor learning

maintains the accuracy of the VOR by modifying the gain and timing of the reflex

whenever retinal image motion is persistently associated with head movements. In the

laboratory, motor learning in the VOR can be acutely induced by pairing head

movements with motion of a visual stimulus.

Two specific hypotheses have been proposed regarding the neural signals that guide

motor learning in the VOR. One suggests that learning is guided by the activity of

Purkinje cells, the output neurons of the cerebellum[1]. The other hypothesis suggests

that learning is guided by climbing fiber input to the Purkinje cells[2-4]. Previous

experiments addressing which neurons carry instructive signals have typically used a

single training condition for increasing VOR gain and a single training condition for

decreasing VOR gain[5, 6]. These two training conditions each elicited Purkinje cell

and climbing fiber signals that carried information about the required direction of

learning, and since the patterns of neural activity were consistent with both

hypotheses, data are needed to provide constraints that could discriminate between the

hypotheses.

The goal of my research is to provide such constraints by recording the patterns of

neural activity present in Purkinje cells and climbing fibers during a broader range of

visual-vestibular stimuli that induce motor learning in the VOR. I induced motor

learning in the VOR by pairing head movements with complex visual stimuli. These

v

novel behavioral manipulations elicited many different combinations of Purkinje cell

and climbing fiber signals, allowing us to evaluate how each of these neural signals

contributes to learning.

My data demonstrated that neither instructive signals in the climbing fibers nor

Purkinje cells are necessary for learning, although either signals appear to be sufficient

to support learning. Additionally, the largest changes in VOR gain occurred when

both signals were present, suggesting that the changes mediated by Purkinje cell-

triggered mechanisms and climbing-fiber triggered mechanisms are additive in their

effects at the behavioral level. These findings are evidence that motor learning in the

VOR is accomplished by parallel and independent operation of climbing fiber-

triggered and Purkinje cell-triggered plasticity mechanisms. If cerebellum dependent

motor learning is supported by the parallel and independent operation of plasticity

mechanisms, similar motor learning need not be accomplished in a stereotyped

fashion, but rather similar motor learning can be achieved by engaging distinct subsets

of plasticity mechanisms each under the control of a unique instructive signal.

vi

PREFACE

Publications

The body of this thesis is in 3 chapters (Chapters 2-4) and will correspond to

publications in which I will be co-first author. Chapter 3 discuss results that have

been published[7]. The second publication is in preparation, and will focus on the

effects of target and background motion on motor learning in the VOR and the

putative instructive signals hypothesized to guide VOR adaptation (Chapter 2). The

data from the generalization of learning studies discussed in Chapter 4 have been

published along with other work in mice from the J. Raymond lab[8].

Structure of collaborations

The behavioral and recording experiments in the thesis involving Monkey L and C

were carried out by myself in Jennifer Raymond’s lab. The experiments conducted on

Monkey E were carried out by Christine Guo, a graduate student in the lab. Based on

her studies in Monkey E, she was the first to recognize the complex interaction

between target slip and the effect of conflicting background motion on climbing fiber

responses, as described in Chapter 2. The data analyses for this project were

conducted in collaboration with Christine, and the writing of manuscripts has been

split between Christine, Jennifer, and me.

Acknowledgments

Many people have played an integral role during my studies at Stanford University.

First, I would like to thank my labmates in the Raymond lab, for their academic

support, and continual affirmation that the end is indeed in sight: Rhea Kimpo, Grace

Zhou, Akira Katoh, Adam Bristol, Edward Boyden, Christine Guo, Barbara Nguyen,

and Soon-lim Shin. Without the technical and laboratory support provided by these

lab managers, the experiments would not have been possible: Kate Wesley, Rachel

Levine, and in particular, Pam Louderback, who’s motherly patience for my constant

clutter in lab was much appreciated. I would like to thank my committee members,

Richard Reimer, John Huguenard, William Newsome, and Eric Knudsen for their

time, brilliant insights, and frank, but diplomatic assessments of my progress. Of

course, I am thankful for the financial support, particularly MSTP at Stanford under

vii

the guidance of Greg Barsh and Seung Kim. As an East Coast transplant, I was

fortunate to make many new friends outside Stanford, and I am grateful for their

companionship and words of encouragement. In particular, I would like to thank these

friends who were a constant presence in my life throughout my entire tenure at

Stanford—Hugh Huynh, Adam Schetky, Phuong Quach, Rick Quach, Rich Stein, and

Euell Tochip. Of course, without the support of my family, I would not be able to

reach any of my personal goals, including graduating from Stanford. I would like to

thank my Mom and Dad for not asking precisely when I am graduating, but instead

allowing me to find my own path, no matter how tortuous it might have been. And I

would like to thank my sister, Sandra, for somehow convincing my parents that

whatever my decisions were, they were the right ones. The partnership, however, that

I am most thankful for, was the one I have with my partner, David Chiu. Thank you

for the amazing memories, and I look forward to many more. And finally, this would

not be at all possible, without the mentorship of my advisor, Jennifer Raymond, who

provided guidance not only on scientific issues, but life issues as well. After every

office meeting with Jennifer, regardless of the topic discussed, I always left a little

more inspired, a little more excited about the scientific endeavors we were

undertaking.

viii

Table of Contents

Chapter 1 Background

1.1 Motor learning . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 1

1.2 Cerebellum-dependent motor learning . . . . . . . . . . . . . 2

1.3 Motor learning in the vestibule-ocular reflex (VOR) . . . . 3

1.4 Two influential but competing VOR learning models . . . . 5

1.5 Challenges to the Marr-Albus-Ito and Miles-Lisberger

Model for motor learning in the VOR . . . . . . . . . . . . . . . . . 9

1.6 Multiple mechanisms underlie motor learning in the

VOR . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.7 The next step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Chapter 2 Effects of visual target and background on motor learning in the VOR

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.1 Training conditions with different combinations of

target and background . . . . . .. . . . . . . . . . . . . . . . . 17

2.2.2 Retinal slip of the target and backgro. . . . . . . . . . . 18

2.2.3 Effects of target and background on tracking eye

movements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.2.4 Effects of target and background on motor

learning in the VOR . . . . . . . . . . . . . . . . . . . . . . . 21

2.2.5 Phase changes induced by consistent and conflicting

training conditions . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.2.6 Motor learning during training conditions at 5 Hz . 27

2.2.7 Effects of target and background on floccular

Purkinje cell simple spikes .. . . . . . . . . . . . . . . . . . . 29

ix

2.2.8 Effects of target and background on floccular

climbing fiber activity . . . . . . . . . . . . . . . . . . . . . . 31

2.2.9 Purkinje cell simple spikes and climbing fiber

signals modulate independently. . . . . . . . . . . . . . . 33

2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.3.1 Learning cues during the induction of motor


2.3.2 Retinal slip velocity and tracking eye movements . 36

2.3.3 Behaviorally-relevant and foveal/parafoveal stimuli 37

2.3.4 Effects of target and background motion on

putative neural instructive signals for motor

learning in the VOR . . . . . . . . . . . . . . . . . . . . . . . . 38

2.3.5 Climbing fibers do not encode net image motion

on the retina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.3.6 Modulation of Purkinje cell simple spikes is

independent of climbing fiber signals . . . . . . . . . . . 42

2.4 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Chapter 3 Elimination of climbing fiber instructive signals during motor learning

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.2.1 Standard training stimuli elicit instructive signals

In climbing fibers . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.2.2 Novel training stimuli eliminate instructive signals

in climbing fibers . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.2.3 Instructive signals carried by Purkinje cell simple

Spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.2.4 Learning in the absence of instructive signals in the

climbing fiber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

x

3.2.5 Learning in the absence of instructive signals in the

simple spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.2.6 Purkinje cell simple spike and climbing fiber signals

together predict behavioral changes . . . . . . . . . . . . 66

3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.3.1 VOR circuit physiology and Marr-Albus-Ito Model 69

3.3.2 Could another population of climbing fibers induce

changes in VOR gain? . . . . . . . . . . . . . . . . . . . . . 70

3.3.3 Other instructive signals for cerebellum-dependent

motor learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.3.4 Multiple instructive signals during cerebellum-

dependent motor learning. . . . . . . . . . . . . . . . . . . . . 75


Chapter 4 Specificity and generalization of motor learning in the VOR

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.2.1 Generalization of learned changes in VOR gain . . . 82

4.2.2 Can VOR gain changes at the non-training

frequencies be explained by a combination of

climbing fiber and Purkinje cell signals? . . . . . . . . 84

4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.3.1 Neuronal population coding schemes to achieve

generalization and specificity . . . . . . . . . . . . . . . . . 88

4.3.2 Generalization of VOR gain increases versus

decreases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.3.3 Patterns of generalization for high versus low

frequency training . . . . . . . . . . . . . . . . . . . . . . . . 89

xi

4.3.4 Generalization and specificity during motor

learning in the VOR. . . . . . . . . . . . . . . . . . . . . . . . 89


Chapter 5 Conclusions and future directions . . . . . . . . . . . . 93

Appendix

Individual climbing fiber and Purkinje cell simple spike responses

for each training stimulus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Bibilography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

xii

List of tables

Table 2.1 Visual vestibular stimuli used to induce motor

learning in the VOR . . . . . . . . . . . . . . . . . . . . . . 18

Table 3.1 Statistical evaluation of climbing fiber and

Purkinje cell simple spike responses to the training

stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Table 3.2 Climbing fibers carrying “gain decrease” versus

“gain increase” signals during x0T/x2BG training

did not differ in any other property . . . . . . . . . . . 73

xiii

List of figures

Figure 1.1 General motor learning scheme. . . . . . . . . . . . . . 1

Figure 1.2 VOR circuitry. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Figure 1.3 Purkinje cell and climbing fiber response during x0

and x2 stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Figure 1.4 Marr-Albus-Ito hypothesis. . . . . . . . . . . . . . . . . . . 6

Figure 2.1 Two independently moving visual stimuli can

create multiple image motions on the retina . . . . . 17

Figure 2.2 Retinal slip of the target and background during

visual-vestibular stimuli used to induce motor


Figure 2.3 Target motion is correlated with gaze velocity. . . . 20

Figure 2.4 Target slip velocity is correlated to gaze velocity . . 21

Figure 2.5 Motor learning in the VOR induced by coherent

motion of target and background . . . . . . . . . . . . . . 22

Figure 2.6 Motor learning in the VOR is correlated to target

slip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 2.7 Conflicting background motion impairs motor


Figure 2.8 Target slip mediates effects of conflicting

Background motion on motor learning in the VOR . 25

Figure 2.9 Changes in VOR phase induced by consistent and

conflicting training conditions . . . . . . . . . . . . . . . . 26

Figure 2.10 Motor learning in the VOR is correlated to target

slip velocity but not gaze velocity at 5 Hz . . . . . . . 28

Figure 2.11 Target and background slip velocities during

training stimuli at 5 Hz . . . . . . . . . . . . . . . . . . . . 29

xiv

Figure 2.12 Purkinje cell simple spike responses are well-

correlated to gaze . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 2.13 Changes in Purkinje cell simple spike responses

during conflicting conditions are correlated to eye

movement responses . . . . . . . . . . . . . . . . . . . . . 31

Figure 2.14 Climbing fiber responses to consistent and

conflicting training conditions . . . . . . . . . . . . . . 32

Figure 2.15 Target slip mediates effects of conflicting

background motion on climbing fiber responses . . 33

Figure 2.16 Training conditions using target and background

motion elicit different combinations of Purkinje

cell and climbing fiber signals . . . . . . . . . . . . . . . 34

Figure 3.1 VOR circuit and Marr-Albus-Ito hypothesis for

VOR learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Figure 3.2 Climbing fiber responses to standard and novel

training stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Figure 3.3 Neural responses and changes in VOR gain elicited

by the x2T/x0BG training stimulus . . . . . . . . . . . . 59

Figure 3.4 Average firing rate of climbing fibers did not vary

across training stimuli . . . . . . . . . . . . . . . . . . . . . . 60

Figure 3.5 Purkinje cell simple spike responses during

training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Figure 3.6 In the absence of instructive signals in the

climbing fibers, learning was correlated with

simple spike responses during training . . . . . . . . . . 64

Figure 3.7 Purkinje cell simple spike and climbing fiber

signals predict learned behavioral changes . . . . . . 67

Figure 3.8 Climbing fiber responses in non-HGVPs during

x0T/x0BG and x0T/x2BG training . . . . . . . . . . . . . 71

xv

Figure 3.9 Climbing fibers with “gain decrease” and “gain

increase” responses during x0T/x2BG training

had similar responses during standard training

stimuli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 3.10 Most of the learning occurs early in the training

session . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 4.1 Motor learning in the VOR at non-training

frequencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Figure 4.2 Generalization indices for consistent and

conflicting training conditions . . . . . . . . . . . . . 83

Figure 4.3 Climbing fiber signals are correlated to learning

at non-training frequencies in Monkey L . . . . . . 85

Figure 4.4 Purkinje cell signals are correlated with learning

at non-training frequencies . . . . . . . . . . . . . . . . . 86

Figure 4.5 Motor learning in the VOR at non-training

frequencies can be predicted by a linear

combination of Purkinje cell and climbing fiber

responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Figure 4.6 Purkinje cell signals contribute more to VOR gain

changes at non-training frequencies . . . . . . . . . . 87

1

Chapter 1 Background

1.1 Motor learning

Motor learning is the process of improving the smoothness and accuracy of

movements. While motor learning is apparent for complicated movements such as

riding a bicycle, or swinging a bat to hit a ball, it is also necessary for calibrating

simple movements such as reflexes, as parameters of the body and environment

change over time.

One approach to understanding motor learning is to trace the effects of stimuli that

induce motor learning through different levels of analysis (Fig. 1.1). At the behavioral

level, non-optimal movements

result in sensory and motor cues

that can be used as “error”

signals to induce learning.

These behavioral error signals

are translated into specific

patterns of neural activity in the

brain. These patterns of activity

act as neural instructive signals

to selectively induce cellular and

synaptic plasticity events, which

in turn alter signal processing in

the learning circuit so that the

behavior is adaptively modified.

In order to link system-level

analyses of learning with cellular analyses of plasticity, a circuit-level understanding

of which patterns of neural activity induce synaptic plasticity in the awake, behaving

animal is needed. This knowledge has been difficult to attain since in most learning

tasks, the precise neural circuits underlying learning have not been delineated, and

therefore the neural instructive signals impinging on a site of plasticity involved in

2

learning are mostly not known. Thus, a first step in understanding what patterns of

neural activity trigger plasticity in vivo, is to identify which population of neurons in a

learning circuit carries the instructive signal used to guide learning. The specific aim

of this thesis is to determine which neurons carry the instructive signals that trigger

plasticity during one specific form of motor learning, adaptation of the vestibulo-

ocular reflex (VOR).

1.2 Cerebellum-dependent motor learning

The cerebellum is a structure critical for motor learning in vertebrates. One of the

most striking features of the cerebellum is its basic architecture, described as being

crystalline due its numerous, repeated modules, each containing the same few cell

types connected in the same manner[9]. While different regions within the cerebellum

receive different inputs, and project to different outputs, the overall uniformity of the

cerebellum suggests that it processes these signals in similar ways.

One of the first and most influential models of cerebellar function was proposed by

Marr and Albus[2, 4]. In their model, mossy fibers via granule cells and their

projections, the parallel fibers, provide sensory and motor input to the cerebellar

Purkinje cells. The Purkinje cells provide the lone output signal of the cerebellum, and

its activity drives specific movements. Over a hundred thousand parallel fibers

synapse onto a single Purkinje cell. The Marr-Albus model proposed that changes in

the strength at specific parallel fiber-Purkinje cell synapses stored stimulus response

associations by linking the inputs with appropriate motor outputs[2, 4].

Another striking feature of the cerebellum is the climbing fiber input —each Purkinje

cell receives input from just one climbing fiber, and the climbing fiber forms a very

strong synapse onto the Purkinje cell in that each presynaptic spike triggers a

postsynaptic spike [10]. However, despite this incredibly powerful connection, the

climbing fiber input makes a small contribution (1-3 Hz) to the overall Purkinje cell

3

firing rate (>60 Hz), suggesting that the function of the climbing fiber is for something

other than ordinary signal transmission.

Marr and Albus proposed that the function of the climbing fiber is to provide an

instructive signal to guide changes at the parallel fiber to Purkinje cell synapse, but

they disagreed on the precise role of the climbing fiber. Marr believed that it was a

positive reinforcer, strengthening parallel fiber to Purkinje cell synapses when the

Purkinje cell output was correct, whereas Albus believed the climbing fibers to carry

an error signal and weakened synapses when the output was incorrect[2, 4].

Consistent with Albus’ prediction, it was later discovered that electrical stimulation of

climbing fibers induced a decrease in synaptic strength, called cerebellar long-term

depression (LTD), at parallel fibers that are active simultaneously[3].

1.3 Motor learning in the vestibulo-ocular reflex (VOR)

One model system to study cerebellum-dependent motor learning is the vestibulo-

ocular reflex (VOR). The VOR system offers a key advantage over other types of

learning: much of the neural pathway underlying the VOR and VOR learning is well

characterized, allowing us to link events occurring in particular neurons and synapses

to circuit-level events during the induction and expression of learning[11].

The VOR is a reflexive eye movement that stabilizes images on the retina by using

vestibular signals to generate compensatory smooth eye movements in the opposite

direction from head motion. The VOR functions to improve vision, but the

performance of the VOR is measured in the dark to isolate eye movements driven by

the vestibular stimuli from eye movements driven by visual stimuli. The performance

of the VOR is characterized by its gain, which is defined as the ratio between eye and

head velocities, and its phase, which reflects the relative timing of eye and head

movements when tested using sinusoidal vestibular stimuli.

4

When the VOR is not well calibrated, head movements result in image motion on the

retina, which impairs visual acuity. When image motion is persistently present during

head movements, a form of motor learning, known as VOR adaptation, gradually

corrects the reflex

and restores proper

function. In the

laboratory, this form

of motor learning is

typically induced by

pairing passive head

movements (e.g.

head-fixed rotation

on a turn table) with

movement of a

single, large visual stimulus. If the visual stimulus moves exactly with the head, a

decrease in the gain of the VOR is induced. Such training stimuli are called “x0”,

because the VOR gain required to stabilize images is zero. If, instead, the visual

stimulus moves exactly opposite to head motion, an increase in VOR gain is induced.

Such training stimuli are called “x2” because the ideal VOR gain would be two (eye

velocity equal to twice head velocity).

The VOR circuitry has been well described (Fig. 1.2)[11]. Neural signals for head

movements are encoded in the vestibular nerve and are processed in parallel pathways.

In the direct pathway, the vestibular afferents project to the vestibular nucleus (VN)

that sends projections to premotor and motor neurons (MN) that control eye

movements. A second pathway is an inhibitory side loop into the flocculus and

ventral paraflocculus of the cerebellum. This pathway conveys vestibular and eye

movement signals via mossy fibers (MF) and granule cells (GrC). The axons of the

granule cells form parallel fibers (PF) that make direct excitatory connections with

Purkinje cells (PC). In addition, Purkinje cells receive excitatory inputs from climbing

5

fibers (CF) that arise from neurons in the inferior olive (IO) and which carry visual

signals related to image motion on the retina. The Purkinje cells are the sole output

neuron of the cerebellum and inhibit VOR interneurons called flocculus target neurons

(FTN) in the vestibular nucleus.

1.4 Two influential but competing VOR learning models

There are two

predominate

hypotheses regarding

the neural learning

rules implemented in

the VOR circuit

during the induction

of learning. Each

proposes that a

particular population of neurons carries the key instructive signal that guides changes

in the circuit. One hypothesis, proposed by Masao Ito (1972) based on the theoretical

works of Marr (1969) and Albus (1971), suggests a learning rule whereby climbing

fibers carry the instructive signals[2, 4, 12]. The other hypothesis, proposed by Miles

and Lisberger (1981), suggests a learning rule whereby signals in Purkinje cells

provide the instructions controlling changes in the VOR circuit[1].

Previous recordings from climbing fibers and Purkinje cells during the induction of

learning provide some support for each of these hypotheses. Climbing fibers and

Purkinje cells each exhibit different responses when sinusoidal head motion is paired

with a visual stimulus moving in a way that increased (x2) versus decreased (x0) VOR

gain[5, 6, 13](Fig. 1.3).

Climbing fibers in the floccular complex are driven by contraversive image motion

and inhibited by ipsiversive image motion. During the x0 stimulus, the climbing

6

fibers increased their firing rate during contraversive head motion (Fig. 1.3), and

decreased their firing rate during ipsiversive head motion. During the x2 stimulus, the

climbing fibers increased their firing rate during ipsiversive head motion, and

decreased their firing rate during contraversive head motion. This difference in the

timing of the climbing fiber firing relative to head movement, (and therefore activity

of vestibular afferents) during x0 versus x2 training reflects the difference in the

timing of contraversive image motion. Cellular studies have indicated that parallel

fibers active around the time of increased climbing fiber activity should be

depressed[3, 14],

whereas parallel fibers

active during decreased

climbing fiber activity

should be potentiated.

Therefore, according to

the Marr-Albus-Ito

model, during x0

training, the climbing

fiber response would

induce selective LTD

of vestibular parallel

fibers that fire during

contraversive head

movements and

selective long-term

potentiation (LTP) of

vestibular parallel

fibers that fire during ipsiversive head movements (Fig. 1.4). On the other hand, the

x2 training should induce selective LTD of parallel fibers that fire during ipsiversive

head movement and LTP of parallel fibers that fire during contraversive movement.

For x0 training, this selective induction of LTD at a particular set of parallel fibers

7

would alter the Purkinje cell simple spike output, causing the Purkinje cells to fire

more in-phase with VOR interneurons in the vestibular nucleus after training as

compared with before training (Fig. 1.4). Since Purkinje cells inhibit VOR

interneurons, the altered Purkinje cell output should tend to cancel the response in the

vestibular interneurons and result in a decrease in VOR gain. In contrast, during x2

training, the selective induction of LTD would alter the Purkinje cell simple spike

output, causing the Purkinje cell to fire more out-of-phase with VOR interneurons

after training, causing an increase in VOR gain (Fig. 1.4).

In summary, the Marr-Albus-Ito hypothesis predicts that climbing fiber activity during

the induction of learning lead to selective LTD and LTP of vestibular parallel fiber

synapses onto Purkinje cells. The engagement of these plasticity events, in turn, alter

Purkinje cell responses during the performance of the VOR in such a way that can

account for the changes in VOR gain.

In contrast to the Marr-Albus-Ito hypothesis, Miles and Lisberger proposed that

activity in the Purkinje cells serve as the neural instructive signal guiding motor

learning in the VOR. Previous recordings of Purkinje cells have provided some

support for the Miles and Lisberger model[15]. Floccular Purkinje cells carry signals

that reflect the eye movements the animals makes to track the visual target[16]. Since

the tracking eye movements were different for x0 versus x2 training, the Purkinje cell

signals were also different. Specifically, during conditions that decrease VOR gain

(x0), there was elevated Purkinje cell activity during ipsiversive head motion, whereas

Purkinje cell activity was reduced during ipsiversive head motion during conditions

that increased (x2) VOR gain (Fig. 1.3). These observations suggest an alternative

learning rule for VOR learning: if increased Purkinje cell activity coincided with

activity in the vestibular afferents during ipsiversive head movement, then synaptic

changes is induced to decrease the gain of the VOR, whereas decreased Purkinje cell

activity coinciding with activity in the vestibular afferents during ipsiversive head

movements would lead to synaptic changes that would increase VOR gain. Since

8

floccular Purkinje cells project to the vestibular nucleus, these synaptic changes are

predicted to occur at the level of the synapses between vestibular afferents and

neurons in the vestibular nucleus (see lightening bolt, Fig. 1.2).

In the Miles and Lisberger hypothesis, Purkinje cell activity during the induction of

learning guide plasticity in the vestibular nucleus. The engagement of these plasticity

mechanisms, in turn, alter the responses of vestibular interneurons during performance

of VOR in such a way that can account for the changes in VOR gain.

Several studies have reported learning-related changes in Purkinje cell activity during

VOR performance [13, 15, 17]. According to Marr-Albus-Ito, these altered signals

result directly from LTD or LTP of synapses onto Purkinje cells from parallel fibers

carrying vestibular input[12]. Miles and Lisberger, on the other hand, suggested that

the altered signals in the Purkinje cells during the VOR reflect altered input to the

cerebellum from an efference copy pathway[1]. Purkinje cell output can influence eye

movements, but the Purkinje cells also receive input from mossy fiber pathways

carrying signals related to eye movement[18, 19]. Thus, there is a feedback loop.

Given this feedback loop, changes almost anywhere in the circuit could be expressed

at any given site. In particular, Miles and Lisberger suggested that learning caused

changes in the vestibular nucleus, which resulted in an altered eye movement

command during the VOR, which in turn was conveyed to the Purkinje cells as an

efference copy signal.

In the past, testing the hypotheses regarding whether climbing fibers or Purkinje cells

carry the instructive signals that guide VOR leaning was conducted by comparing

signals in climbing fibers and Purkinje cells during one stimulus that increased VOR

gain (x2) and one stimulus that decreased VOR gain (x0). Since the patterns of neural

activity present during these stimuli were consistent with both hypotheses, more data

are needed to test each hypothesis individually, and this dissertation provides such

data.

9

In summary, these hypotheses make distinctly different predictions regarding the role

of the cerebellum in motor learning. First, each makes a different prediction on which

neurons carry the instructive signal guiding motor learning. Marr-Albus-Ito contends

it is the climbing fibers, whereas Miles and Lisberger argue it is the simple spike

output of the Purkinje cells. Second, each hypothesis makes a different prediction

regarding the site of plasticity. Marr-Albus-Ito proposes that the site of memory is at

the parallel fiber to Purkinje cell synapse in the cerebellar cortex. Miles and Lisberger

assume the changes to be in the vestibular nucleus. Last, each hypothesis makes

different predictions on the source of learning-related changes in Purkinje cell simple

spike activity during VOR performance after learning has occurred. Marr-Albus-Ito

believed that the changes in Purkinje cell are the direct result of plasticity in the

cerebellar cortex. Miles and Lisberger, on the other hand, believed that the changes in

the Purkinje cell simple spike during VOR performance are reflective of the efference

copy of the altered eye movement command created in the vestibular nucleus.

1.5 Challenges to the Marr-Albus-Ito and Miles-Lisberger model for motor

learning in the VOR

There are several challenges to each of these influential models as the model for motor

learning in the VOR. The biggest challenge to the Marr-Albus-Ito hypothesis is the

observation that after motor learning is induced in the VOR, changes in signal

processing by the Purkinje cells cannot be predicted by the neural learning rules

governing cerebellar LTD[16]. The Marr-Albus-Ito hypothesis predicts that the

learning-related changes observed in Purkinje cell signals during VOR after learning

result directly from LTD or LTP of synapses onto Purkinje cells from parallel fibers

carrying vestibular input[12]. However, the altered responses may be attributed to

either a change in the vestibular or efference copy input to the Purkinje cells. To

assess how much of the altered response was attributed to just the vestibular inputs, an

additional test stimulus besides the VOR was used. The test was to record Purkinje

cells while the animal cancelled its VOR (VORC) by tracking a target that moved

10

exactly with him during sinusoidal head turns. During VORC, the eye velocity was

close to zero throughout the head movement, so there should be little to no firing rate

modulation in the efference copy pathway, and therefore the response in the Purkinje

cell during VORC was taken as a measure of the strength of its vestibular inputs.

Several studies using this method revealed that learning-related changes in the

vestibular sensitivity were in the wrong direction to account for the change in VOR

gain [13, 15, 20], and in the wrong direction from what would be predicted if the

climbing fiber responses observed during induction of learning produced LTD in the

vestibular parallel fibers active simultaneously.

The major challenge to the Miles and Lisberger hypothesis is that adaptive gain

changes can occur under some conditions where Purkinje cell signals cannot

discriminate between stimuli that increase and decrease VOR gain, and therefore

provide no useful instructive signals to guide the appropriate plasticity[21]. Typically,

VOR adaptation is induced with 0.5 Hz sinusoidal x0 and x2 visual-vestibular stimuli,

and during these stimuli, both climbing fibers and Purkinje cells carry potentially

useful instructive signals (Fig. 1.3). VOR adaptation can also be successfully induced

with 5 Hz sinusoidal x0 and x2 visual-vestibular stimuli[22]. However, recordings of

Purkinje cells and climbing fiber signals during the induction of learning at 5 Hz,

revealed that climbing fibers carried information about the direction of learning, but

Purkinje cells did not[6]. This indicated that Purkinje cells cannot provide the

instructive signals that guide learning induced with high frequency stimuli.

1.6 Multiple mechanisms underlie motor learning in the VOR

In the last several years, evidence using in vitro preparations have demonstrated many

plasticity mechanisms in the cerebellum and related circuitry, suggesting a wide

variety of plasticity mechanisms are available to be implemented within cerebellum-

dependent motor learning tasks[23]. One possible resolution to the challenges faced

by the Marr-Albus-Ito and Miles-Lisberger model is both these models as well as

other mechanisms contributes to motor learning in the VOR.

11

A model that incorporates distributed learning and multiple plasticity mechanisms was

first proposed to explain why the vestibular sensitivity of Purkinje cells changes in the

wrong direction to account for the change in VOR gain. Lisberger and Sejnowski

proposed that having changes only in the vestibular nucleus would cause unstable eye

movements due to a positive feedback loop within the VOR circuit[24]. Since

Purkinje cells can drive eye movements, and signals related to these eye movement

commands then feed back to drive more Purkinje cell activity (Fig. 1.2), unstable eye

movements would result after learning-related changes in the vestibular nucleus have

been induced. However, additional changes in the cerebellar cortex, namely the

observed changes in vestibular sensitivity are in the appropriate direction to maintain

proper movement dynamics.

The gain of the VOR can be adaptively increased or decreased, and the original Marr-

Albus-Ito hypothesis attributes a single plasticity mechanism (cerebellar LTD) at

different sets of parallel fiber-Purkinje cell synapses to account for VOR gain

increases and decreases. If the model was accurate, then increases and decreases in

VOR gain should have similar properties stemming from their shared mechanism.

However, several studies have demonstrated asymmetries in the behavioral properties

of VOR gain increases and decreases, suggesting that they depend on different

plasticity mechanisms. For example, increases in VOR gain passively decayed

quicker than decreases in VOR gain[25], increases in VOR gain can be actively

reversed more readily than decreases in gain[26], and increases in gain generalize less

than decreases[8], when learning is measured in a context different from one used for

training. In addition, several pharmacological and genetic studies have also

demonstrated that VOR gain increases and decreases are dependent on different

cellular mechanisms—inhibiting nitric oxide receptors, a component in the induction

of cerebellar LTD, impairs gain increases but not decreases in goldfish[27]. And mice

lacking CAMKIV, a required molecule for long-lasting LTD significantly impairs

long-term retention of VOR gain increases, but not decreases[28].

12

1.7 The next step

During the last several decades, an attempt to discriminate between the Marr-Albus-

Ito and Miles-Lisberger hypothesis has dominated research on motor learning in the

VOR. However, with recent in vitro and in vivo evidence strongly suggesting a role of

multiple mechanisms, the question now shifts from, “Which plasticity mechanism is

correct?” to “How may a single plasticity mechanism contribute to motor learning in

the VOR?”

The goal of the this thesis project is to help elucidate how plasticity mechanisms, as

proposed by the Marr-Albus-Ito and Miles-Lisberger models, may contribute to motor

learning in the VOR. The overall experimental goal is to correlate the presence and

absence of motor learning in the VOR with the presence and absence of neural

instructive signals carried by the climbing fibers and Purkinje cells during the

induction of learning. By correlating learning with the available neural instructive

signals, I hope to address fundamental questions regarding the necessity, and possibly

sufficiency of each neural instructive signal to learning. Answering these fundamental

questions will yield powerful insights into the learning algorithms utilized by the

cerebellum, and its learning capacity.

The following thesis is divided into three major chapters. The first chapter will focus

on the effects of pairing complex visual stimuli with head movements to induce motor

learning in the VOR. These experiments were designed to study how systematic

changes in the sensory and motor stimuli known to guide motor learning in the VOR

affect the putative neural instructive signals and motor learning. The second chapter

will focus on a specific subset of the training conditions that allowed us to test the

necessity of the climbing fiber instructive signals for motor learning. In addition, this

chapter examines how Purkinje cell and climbing fiber instructive signals may

together account for learning. Finally, the last chapter examines how training with

complex visual stimuli can affect the generalization of motor learning in the VOR,

13

specifically examining whether learning-related changes at the training frequency may

be expressed at non-training frequencies. If learning can be expressed at non-training

frequencies, this chapter examines whether Purkinje cell and climbing fiber instructive

signals may account for these changes.

14

Chapter 2 Effects of visual target and background on motor learning in the VOR

2.1 Introduction

The cerebellum plays a critical role in motor learning, the process of improving the

smoothness and accuracy of movements. The dominant theory over the past several

decades postulated that the climbing fiber input to the cerebellum encodes an error in

the motor output, and that this neural signal guides plasticity in the cerebellum in such

a way that the error in motor output is subsequently reduced[2, 4, 12]. Several studies

have provided evidence for this hypothesis, namely that under training conditions that

induce motor learning, climbing fiber activity is well-correlated to errors in motor

performance[6, 29]. These studies, however, were mostly carried out in well-

controlled laboratory settings, where learning was induced with robust, well-defined

errors. In contrast, in the real-world, motor learning typically occurs under a more

complex learning environment, where multiple, and at times conflicting errors in

motor performance, may be present. The effects of multiple errors on learning and

neural instructive signals are largely unknown.

The oculomotor system is an ideal motor system for laboratory studies because eye

movements can be easily quantified, and the neural pathways underlying the behavior

are well understood[11]. The main goal of any oculomotor system is to stabilize

images on the retina, and if eye movements are not well-calibrated, retinal slip or

image motion occurs, and cerebellum-dependent motor learning is induced to adjust

the eye movement response so the retinal slip is reduced. Climbing fibers in the

floccular complex, the part of the cerebellum thought to mediate this form of motor

learning, carry information related to retinal slip[5]. Previous experiments induced

motor learning with coherent motion of a visual stimulus on the retina[13, 16, 22].

However, this simplified condition is not reflective of the real-world, where animals

are likely to encounter multiple image motions. For example, tracking a small visual

target would result in opposing image motions of the target and background on the

retina. Or during motion parallax, the differential displacement of near and far objects

15

may result in opposing image motions on the retina. Little is known how complex,

opposing image motions may affect oculomotor learning and putative neural

instructive signals.

We examined the effects of a complex learning environment on motor learning and

putative neural instructive signals, using a cerebellum-dependent motor learning

paradigm, motor learning in the vestibulo-ocular reflex (VOR). The VOR stabilizes

images on the retina during head movements by generating compensatory eye

movements in the opposite direction of the head motion. When VOR is not

appropriately calibrated, retinal slip is persistently present during head movements,

and motor learning is induced to adjust the amplitude and timing of the eye movement

responses. Motor learning in the VOR can be induced in the laboratory by pairing

head movements with motion of single coherent visual stimulus. Neural recordings

during these experiments have demonstrated that climbing fiber activities carry

sensory signals related to retinal slip, and Purkinje cell activities carry motor signals

related to eye movements[18, 30]. Both these signals have been suggested to be

neural instructive signals guiding plasticity during the induction of motor learning in

the VOR.

The training condition with a coherent visual stimulus has been used successfully in

understanding the basic characteristic of the neural circuit underlying VOR learning.

Here we studied motor learning in the VOR with paradigms more complex than

traditional training paradigms to determine the effects of multiple, conflicting image

motions on the neural instructive signals and motor learning in the VOR. In

particular, we induced learning with the simultaneous presentation of two visual

stimuli moving independently-- a small visual target, and a large-field visual

background. The use of two visual stimuli moving independently permitted us to

design training conditions where the image motions of the target and background are

in the same direction on the retina, producing "consistent" image

motions. Alternatively, we can design training conditions where the image motion of

16

the target and background are in opposite direction on the retina, therefore producing

"conflicting" image motions. We recorded climbing fiber and Purkinje cell simple

spikes during the induction of learning with training conditions eliciting consistent and

conflicting image motions. An understanding of the effects of multiple image motions

on the putative neural instructive signals may provide insights on how learning is

selectively induced under more complex environments.

17

2.2 Results

2.2.1 Training conditions with different combinations of target and background

In our experiments, we paired head motion with motion of two visual stimuli—a

visual target (0.5˚ diameter) that the animal was trained to track, and a large visual

background (20˚ x 30˚). The target and background moved independently from each

other. Figure 2.1 illustrates

two different combinations of

target and background. During

these training conditions, the

head was moving with a

sinusoidal velocity profile at

0.5 Hz ± 10 ˚/s. In both the

x0T/x0BG and x0T/x2BG

training condition, the visual

target and head were moving

together (brown and purple

lines). Since the target

motions were the same during

both training conditions, the

eye movements (blue lines)

were also comparable. However, the monkeys were not perfectly tracking the target,

resulting in small, but significant retinal slip of the target (typically less than 3 ˚/s,

purple lines). In the illustrated pair of training conditions, the background was moving

differently. In the x0T/x0BG training stimulus, the background was moving exactly

the same as the target; whereas during the x0T/x2BG training stimulus, the

background was moving with the same speed as the target, but in the opposite

direction (compare gray and purple lines labeled target and background). Thus, the

resulting background slip during each condition was remarkably different.

18

We tested 22 different

target and background

combinations in

Monkey L, 16

different combinations

in Monkey C, and 13

different combinations

in Monkey E (Table

2.1, Methods). In

each case, the

vestibular stimulus

was sinusoidal head

movement at 0.5 Hz,

± 10 ˚/s, with the

exception of 6 training

stimuli in monkey L

that were tested at 5

Hz, ± 10 ˚/s. (Table 2.1, Methods).

2.2.2 Retinal slip of the target and background

In our experiments, the animals were trained to track the visual target while ignoring

motion of the background. The presence of two visual stimuli moving independently,

created two different types of retinal image motion—image motion of the visual target

and image motion of the background. The target slip velocity was calculated by

determining the difference between gaze velocity and target velocity. Gaze velocity

was the linear addition of the eye and head velocities, and reflected where the animal

was looking in earth-fixed coordinates. Target slip velocity was a direct measure of

how well the animal tracked the target. The better an animal tracked, the smaller the

target slip velocity on the retina. In Figure 2.2, the target slip and background slip

during peak head velocity were plotted for each 0.5 Hz training stimulus.

19

The slip velocities reported in this chapter are the amplitude of target and background

slip that occurred during peak head velocity (see Methods). If peak retinal slip

occurred in the same direction as head motion, then the retinal slip velocities were

assigned positive values. If peak retinal slip occurred opposite direction of head

motion, then the retinal slip velocities were assigned negative values. If both the

target and background slip occurred in the same direction on the retina relative to head

motion, the conditions were called “consistent” conditions (white quadrants, Fig. 2.2).

If a training condition elicited target slip on the retina opposite in direction of

background slip during head motion, then the stimulus was called a “conflicting”

condition (blue quadrants, Fig. 2.2).

2.2.3 Effects of target and background on tracking eye movements

In our many training conditions, we paired head movements with target motion to give

us a range of gaze velocity responses. Recall that gaze velocity is the linear addition

of head and eye velocities, and reflects where the animals are looking in earth fixed

coordinates. Since animals were trained to track a visual target, target motion was

correlated to gaze velocity. Figure 2.3 plots the amplitude of gaze velocity during

peak head velocity (see Methods) during training conditions with different target

20

motion. Positive values were

assigned if gaze was in the same

direction as head movements;

whereas negative values were

assigned to gaze in the opposite

direction of head movements. For

example, the x0T/x0BG training

stimulus paired a target and

background moving in the same

speed and direction as the head.

Therefore, the gaze of a monkey

tracking the target would be in the

same direction as the head and

was assigned a positive value. On the other hand, the x2T/x2BG training condition

paired a target and background both moving in the opposite direction but at the same

speed as the head. In this training condition, the gaze of a monkey tracking a target

would be in the opposite direction of head motion, and therefore assigned a negative

value. During x1T/x1BG, the visual stimulus was stationary relative to the world, so a

monkey tracking the target maintained central fixation throughout head rotation, and

no gaze velocity was elicited.

The target slip elicited by each training condition was correlated to gaze velocity.

Figure 2.4 plots the relationship between gaze velocity and target slip velocity induced

by each training condition. In general, as gaze velocity increased, target slip velocity

increased. Since gaze velocity was determined by target motion (Fig. 2.3), and the

target slip reflected how well the animal was tracking the target, the relationship

between gaze velocity and target slip velocity demonstrate that as the target moved

faster, animals became less effective at tracking the target.

21

In a subset of our training conditions, the background slip was in the opposite

direction of the target slip on the retina. During these “conflicting” conditions,

tracking eye movements were affected (gray diamonds, Fig. 2.4). In Monkey L, the

presence of a conflicting background decreased target slip velocity, revealing that

Monkey L’s tracking eye movements became more effective in the presence of

conflicting background motion. In Monkey C, the presence of a conflicting

background had an opposite effect to that on Monkey L, in that target slip velocity

increased. In Monkey C, the presence of conflicting background motion made its

tracking eye movements less effective. In Monkey E, the presence of conflicting

background motion had no effect on tracking eye movements. Although the presence

of a conflicting background affected tracking eye movements in Monkey L and

Monkey C, the absolute effects were small, in that the gaze velocity during conflicting

training conditions were similar to gaze velocity elicited by consistent training

conditions.

2.2.4 Effects of target and background on motor learning in the VOR

We tested the effectiveness of training conditions at inducing motor learning in the

VOR. Motor learning in the VOR was induced by presenting a training stimulus for

either 1 h (Monkey E) or 2 h (Monkey C and Monkey L). VOR learning was assessed

22

comparing eye movement responses to head movements in complete darkness before

and after training (see Methods for more details).

For training conditions that elicited consistent retinal slip of the target and

background, learned changes in VOR gain (∆ VOR gain ) were well-correlated with

target motion, as shown in previous experiments [6, 31, 32]. Training that paired

target and background slip in the same direction as head movements (x0T, x0.5T

conditions) induced a decrease in VOR gain, whereas training conditions that paired

target and background slip in the opposite direction as head movements (x2T, x1.5T

conditions) induced an increase in VOR gain (Fig. 2.5)

The critical difference between consistent training conditions that induced a gain

increase versus gain decrease is the direction of target slip on the retina. If the target

slip was in the same direction as head motion, then the VOR gain was too small to

keep the target on the retina, and a gain increase was induced, whereas if the target

slip was in the opposite direction as head motion, then the VOR gain was too large,

and a gain decrease was induced. To evaluate if learning was affected by the

amplitude of target slip, we plotted the amount of learning according to the target slip

velocities elicited by each consistent training condition (Fig. 2.6).

23

A linear regression analysis demonstrated that learning was well-correlated to target

slip velocities (R2 = 0.94 for Monkey L, R2 = 0.97 for Monkey E, R2 = 0.50 for

Monkey C). Monkey C had variable data, particularly for x2T/x2BG training

condition, that either elicited moderate gain increases or no change in VOR gain. This

consistent training condition has been previously used in many studies and typically

induced a robust increase in VOR gain as demonstrated by Monkey L and Monkey E

[22, 31]. Monkey C’s inability to effectively learn may be due to its ability to track

the visual stimulus throughout the 2 h of training. If the animal looks away from the

moving visual stimulus during training, it will be receiving at x1 stimulus, which can

trigger changes within the circuit to offset changes triggered by either x0 or x2

training stimulus. Interestingly, Monkey C had a harder time tracking a x2 stimulus

than a x0 stimulus (note larger error bars for Monkey C for target slip during negative

gaze velocities that correspond to gain up stimuli in Figure 2.4), and it is the x2

consistent training conditions that had the most variable amounts of learning.

For each animal, we fitted the data points with a linear regression and derived the

regression coefficient which represented the rate of learning for each animal (Fig. 2.6,

black lines). The regression coefficients were -30.3 %/°/s for Monkey L, -16.2 %/°/s

for Monkey E, and -7.6 %/°/s for Monkey C. The different rates of learning most

likely reflect intrinsic differences in learning capacity, as well as differences in

training conditions (e.g. 1 h training for Monkey E, 2 h training for Monkey L) which

were optimized to each animal’s work abilities.

24

To combine the animals’ datasets for further analyses, we normalized each animal’s

learning data by dividing the ∆ VOR gain by the learning rate as calculated in Fig. 2.6.

As expected a linear regression analysis applied to the normalized ∆ VOR gain

yielded a coefficient of “1.00” (Fig. 2.7, left). It is important to keep in mind that the

training conditions used for this analysis are the consistent training conditions

comprised of a coherently moving target and background.

We next evaluated how conflicting target and background may affect motor learning

in the VOR. The ∆ VOR gain induced by each conflicting training condition was

normalized by the learning rates for each animal (Fig. 2.7, right). If the conflicting

background had no effect on learning, we would expect the learning to be similar to

learning induced by consistent training conditions, and a regression analysis on the

normalized ∆ VOR gain induced by conflicting training conditions should yield a

coefficient of 1.00. In this situation, the amount learning would be predicted by target

slip alone. However, a regression analysis on the normalized ∆ VOR gain for

25

conflicting training conditions was 0.29, demonstrating that conflicting background

slip impaired learning by approximately 70% relative to the learning induced by

consistent training conditions.

The correlation between target slip and learning induced by conflicting training

conditions (R2 = 0.42) was not as robust as the correlation between target slip and

learning induced by consistent training conditions (R2 = 0.73), suggesting that the

learning impairment is not a global 70% reduction at all target slip velocities. To take

a closer look at the effects of the conflicting background on learning, we grouped our

training conditions according to

their target motion (x0, x2 for

Monkey L and Monkey C; x0, x0.5,

x1, x1.5, x2 for Monkey E). For

each target motion, we calculated

the difference in learning between

the consistent and conflicting

training conditions, and divided the

difference by the learning induced

with consistent conditions. These

values represent the effect of the

conflicting background motion on

learning for each target motion. We

plotted the effect of conflicting

background motion and the average target slip associated with each target motion (Fig.

2.8). In general, when the target slip was small (when the animal was tracking well),

the effect of the conflicting background was large. However, when the target slip was

larger (when the animal was not tracking well), the effect of the conflicting

background diminished.

26

2.2.5 Phase changes induced by consistent and conflicting training conditions

Changes in VOR gain were accompanied by changes in the phase of the VOR. The

VOR phase is a measure of when peak eye velocity occurs relative to peak head

velocity during the VOR. If the peak eye velocity response to head motion occurred

before peak head velocity, then there was a phase lead. If peak eye velocity response

to head motion occurred after peak head velocity, then there was a phase lag. Visual-

vestibular stimuli that induced VOR gain changes also induced VOR phase changes.

A change in VOR phase was calculated as the phase of the VOR after learning minus

the VOR phase before learning. Typically, VOR gain changes induced at the training

frequency were not accompanied by a change in VOR phase [22]. However, changes

in VOR gain at non-training frequencies were accompanied by a change in phase.

Figure 2.9 plots the phase profiles for consistent and conflicting training conditions.

In general, increases in gain (x2 training conditions, blue) induced an increase in phase

lag at higher test frequencies, whereas decreases in gain (x0 training conditions, red)

induced an increase in phase lead at higher test frequencies. In Monkey C, the x0

training conditions did not elicit consistent phase leads, so those results were

replicated in Monkey E. x0 and x2 training conditions did not induce different

changes in the phase of the VOR measured at 0.2 Hz. Phase changes during

conflicting training conditions were similar to consistent training conditions for x2

(compare light blue and dark blue), however, x0 conflicting training conditions (light

pink) induced little phase changes at the higher testing frequencies.

27

2.2.6 Motor learning during training conditions at 5 Hz

During the induction of motor learning in the VOR with 0.5 Hz visual-vestibular

stimuli, the target slip velocity was correlated with learning (Fig. 2.6). Since gaze

velocity and target slip velocity were correlated (Fig. 2.4), learning can also be

correlated to gaze velocity. The correlations between VOR learning and target slip

and gaze velocities suggest that the effects of target motion on VOR learning may be

mediated by either the retinal slip it creates or by the eye movements it elicits.

To study whether motor learning in the VOR can be guided by target slip velocity, but

not gaze velocity, we tested in Monkey L a select subset of the target and background

combinations moving at 5 Hz, with a peak velocity of ± 10 deg/s. At 5 Hz, the

monkey was unable to track the visual stimuli. The position excursion of the target

moving at 5 Hz was small (± 0.3 deg), so in order to get the juice reward, the monkey

simply attempted to maintain fixation on the target. At 5 Hz, the eye movements were

similar for x0 and x2 training conditions, and therefore, the gaze velocity were the

same during these training conditions. Figure 2.10 plots the background velocity

(red), target velocity (blue), and gaze velocity (green) against the learning induced by

six different training conditions at 5 Hz for Monkey L. The median amount of

learning induced by a training condition was plotted on the vertical axis, and the error

bars represented the range of gain changes that were induced.

28

Motor learning induced by 5 Hz training conditions was not well correlated to

background slip velocity or gaze velocity. However, target slip velocity was

correlated to the induced changes in VOR gain. If target slip occurred in the same

direction as head motion, a decrease in VOR gain was induced. If target slip occurred

in the opposite direction of head motion, an increase in VOR gain was induced.

Therefore, target slip was capable of influencing learning, and the effects of the target

on learning may not necessarily be mediated by its effects on eye movements.

During 5 Hz stimuli, the monkeys were unable to track the visual target. The eye

movement responses during x0 and x2 training conditions at 5 Hz resulted in nearly

equal target and background slip velocities. Figure 2.11 plots target and background

slip velocities, and the learning induced by each of the 6 different visual-vestibular

stimuli tested at 5 Hz.

29

Similar to 0.5 Hz stimuli, pairing of head movements with a target alone was

sufficient to induce robust changes in VOR gain. VOR gain during all 6 stimuli went

in the direction predicted by the

target. The x2T/x0BG training

condition elicited greater target slip

than background slip. This was the

only conflicting condition at either 0.5

or 5 Hz where the target slip velocity

was bigger than background slip

velocity. The x2T/x0BG training

condition at 5 Hz induced increases in

gain, suggesting that motor learning in

the VOR need not be in the direction

predicted by the visual stimulus

eliciting the smallest retinal slip

velocity.

2.2.7 Effects of target and background on floccular Purkinje cell simple spikes

To analyze the effects of consistent and conflicting training conditions on the putative

neural instructive signals during motor learning in the VOR, we recorded Purkinje

cells in the floccular complex of the same three rhesus monkeys used for the

behavioral experiments. We focused our analysis on a subclass of Purkinje cell

neurons, known as Horizontal Gaze Velocity Purkinje cells (HGVPs), which have

been implicated in motor learning in the VOR [11, 19]. Spikes in the climbing fiber

reliably trigger calcium spikes, called complex spikes, in its Purkinje cell targets in a

one-to-one manner, therefore we used complex spike activity as a measure of activity

in its climbing fiber input [10, 33].

Individual neural responses to all training conditions for each monkey are supplied in

the appendix. The peak amplitude of the neural response elicited by each visual-

30

vestibular stimulus, and its phase relative to head velocity were calculated. To

systematically compare neural responses to many different training conditions, we

compared responses at a particular phase of head movement, and we report the neural

response aligned with peak head velocity (see Methods). Responses with positive

values correspond to increased firing during peak ipsiversive head movement, and

responses with negative values correspond to increased firing during peak

contraversive head movement.

The simple spike output of the Purkinje cells has been proposed to be a neural

instructive signal for motor learning in the VOR [1]. As shown in previous studies,

the simple spike activity was well-correlated to gaze velocity (Fig. 2.12). Gaze in the

same direction as head motion (positive gaze) elicited increases in Purkinje cell simple

spike firing rate during ipsiversive head motion, whereas gaze in the opposite direction

as head motion (negative gaze) elicited decreases in simple spike firing rate during

ipsiversive head motion.

The presence of a conflicting background had small effects on the tracking eye

movements in Monkey L and Monkey C. These effects were opposite in nature—in

Monkey L, presence of a conflicting background improved tracking eye movements,

whereas in Monkey C, presence of a conflicting background impaired tracking eye

movements. These effects can be reflected in the gaze velocity elicited by the

31

conflicting training conditions (Fig. 2.4). In Figure 2.13, we compared gaze velocities

elicited by conflicting and consistent training conditions. For each target motion, the

averaged gaze velocity elicited by

the conflicting training conditions

was normalized by dividing the

value by the averaged gaze

velocity elicited by consistent

training conditions. For Monkey

L, values for normalized gaze

velocity for each target motion

were greater than 1.0, whereas for

Monkey C, the normalized gaze

velocities were less than 1.0.

Thus, for Monkey L, conflicting training conditions increased gaze velocities relative

to consistent training conditions, whereas for Monkey C, conflicting training

conditions decreased gaze velocities. When we examined the change in Purkinje cell

simple spike firing rate modulations during conflicting training conditions relative to

the firing rate modulations elicited by the consistent training conditions (abscissa, Fig.

2.13), we observed opposite effects of the conflicting background on Purkinje cell

responses between Monkey L and Monkey C. For Monkey L, conflicting conditions

increased Purkinje cell responses relative to consistent conditions, whereas for

Monkey C, conflicting training conditions decreased Purkinje cell responses. Thus, the

changes in tracking eye movements as measured by gaze velocity during conflicting

training conditions parallel the observed changes in Purkinje cell simple spike activity.

2.2.8 Effects of target and background on floccular climbing fiber activity

Floccular climbing fiber activity is thought to carry information regarding retinal slip

[5]. During consistent training conditions, climbing fiber activity was well-correlated

to target slip—target slip in the same direction as the head (positive slip) elicited

increases in climbing fiber activity during contraversive head movements, whereas

32

target slip in the opposite direction as the head (negative slip) elicited increases in

climbing fiber activity during ipsiversive head movements (Fig. 2.14, gray diamonds).

Climbing fibers have an average spontaneous firing rate of approximately 1 Hz, and

the maximal firing rate modulation was approximately ± 1 sp/s.

Presence of a conflicting background reduced the firing rate modulation of climbing

fibers. With few exceptions, the climbing fiber responses remained in the same

direction as the responses elicited by consistent training conditions (Fig. 2.14, white

diamonds). Floccular climbing fibers are typically thought to be driven by

contraversive image motion[5], and the phase of the climbing fiber response relative to

head movement correlated to when contraversive image motion occurred during the

visual-vestibular stimuli. During conflicting training conditions, the retinal slip of the

target and background were moving in opposite directions. If the climbing fibers were

more sensitive to the background slip than target slip, then we would expect during

conflicting conditions, the climbing fiber response to be out-of-phase relative to the

consistent conditions. We observed this effect for only a small subset of the

conflicting training conditions (x0.5T/x1.5BG and x1T/x0BG in Monkey E),

suggesting for the most part target slip was more effective at driving climbing fiber

responses than background slip.

33

For Monkey E, the x0.5T/x1.5BG and

x1T/x0BG conflicting training

conditions elicited relatively smaller

amounts of target slip (0.58 and 0.08

deg/s, respectively) compared to

x0T/x2BG and x2T/x0BG conflicting

training conditions (0.92 and 1.06

deg/s, respectively). We suspected

that the amplitude of target slip may

gate the effect of the conflicting

background on climbing fiber

modulation. Indeed, when we

examined the effects of the conflicting background on climbing fiber responses

according to the elicited target slip, we find that the conflicting background has a

larger effect on climbing fiber activity when the target slip velocity was small (Fig.

2.15).

Interestingly, the effects of the conflicting background on learning were paralleled by

its effect on climbing fiber activity (Fig. 2.8 and Fig. 2.15). In both cases, the amount

of target slip mediated the effect of the conflicting background, with the largest effect

of the conflicting background when the target slip was small.

2.2.9 Purkinje cell simple spikes and climbing fiber signals modulate

independently

Cerebellar researchers have proposed that the climbing fiber signals directly modulate

the simple spike signals[34], resulting in antiphasic simple spike and complex spike

activity. If climbing fiber activity directly affected simple spike modulation, then we

would expect the background effect on climbing fiber signals should also affect the

Purkinje cell simple spike response.

34

Our experiments can directly test this hypothesis, since our training conditions elicited

different combinations of climbing fiber and Purkinje cell responses. We can test

whether Purkinje cell simple spike and climbing fiber responses covary, as predicted if

climbing fiber activity modulates Purkinje cell simple spike activity. In our set of

training conditions, we created pairs of training conditions where climbing fiber

responses were the same, but Purkinje cell simple spike responses were different, or

vice versa. For example, in Monkey L, the x2T/x2BG training condition (Fig. 2.16,

green datapoint) elicited statistically similar Purkinje cell responses as the x2T/x0BG

training condition (Fig. 2.16, blue datapoint, p = 0.51, paired t-test), but statistically

different climbing fiber responses (p < 0.001, paired t-test). Likewise, in Monkey L,

the x2T/x0BG (Fig. 2.16, red datapoint) elicited similar climbing fiber responses to the

x0T/x2BG training condition (Fig. 2.16, blue datapoint, p = 0.50, paired t-test), but

different Purkinje cell responses (p < 0.001, paired t-test). Therefore, the covariation

between Purkinje cell simple spike responses and climbing fiber responses can be

broken using our behavioral manipulations.

Further evidence that climbing fiber and Purkinje cell signals modulate independently

comes from the x0T/x2BG training conditions in Monkey L and Monkey E. The

x0T/x2BG training condition elicits no climbing fiber modulation, yet Purkinje cell

simple spikes remain robustly modulated (p = 0.39, Monkey L; p = 0.84, Monkey E;

one sample t-test, Fig. 2.16). During the x0T/x2BG training condition, the climbing

35

fiber activity is similar to its spontaneous activity (see Chapter 3). Thus, something

other than climbing fiber activity must be driving Purkinje cell simple spike

modulation during this training condition. Since the simple spike modulation was

well-correlated to gaze (Fig. 2.12), the modulation was most likely driven by eye

movement inputs.

36

2.3 Discussion

2.3.1 Learning cues during the induction of motor learning in the VOR

To acutely induce motor learning in the VOR in previous experiments, head

movements were paired with motion of a single visual stimulus, providing image

motion of a single stimulus in one direction on the retina. However, in a more natural

environment with multiple visual cues, multiple and at times conflicting image

motions on the retina may be present. The preceding set of experiments induced

motor learning in the VOR with multiple visual stimuli, in the hope of understanding

the effects of multiple image motions on putative neural instructive signals and motor

learning in the VOR. We find that motor learning in the VOR is affected by both

target and visual background motion. Several factors may influence the potency of the

target and background at inducing learning.

2.3.2 Retinal slip velocity and tracking eye movements

At low frequency, imperfect tracking of the visual target result in small-velocity

retinal slip of the target. During presentation of the visual background, there is

simultaneous retinal slip of the background that can be either in the same or opposite

direction as target slip During conditions of conflicting, or opposing slip, learning is

strongly influenced by the background, although in most conditions we tested, the

target predicted the direction of learning. Since at these lower frequencies, the

animals have the ability to track the target, the slip velocities of the target were smaller

than the slip velocities of the background. Several lines of earlier evidence suggest

how the target may exert its influence: First, the visual target controls tracking eye

movements that drives the simple spikes of the Purkinje cells, another putative

instructive signal shown to be well correlated to learning when present[1]. Second,

climbing fiber signals, a putative instructive signal, can be driven my image motion of

a target. Third, climbing fiber signals may be driven by the eye movements used to

track the target[30]. And last, the climbing fiber tend to be tuned to image motion of

low slip velocities, therefore, the low image slip of the target may be more effective at

driving climbing fiber signals relative to the background[5].

37

Our results demonstrate that the effects of the target on VOR motor learning is not due

to only tracking eye movements. First, during training conditions with similar eye

movements (e.g. compare x0T/x0BG versus x0T/x2BG), but very different

combinations of target and background slip, different amounts of learning was

induced, suggesting that eye movements are not the lone behavioral learning cue for

VOR learning. While our experiments exclude the possibility that eye movements

during 0.5 Hz stimuli is the lone behavioral cue guiding motor learning in the VOR, it

does not exclude an eye movement contribution to learning.

The experiments at 5 Hz also demonstrate that motor learning in the VOR are not

guided by images with the lowest slip velocities. At 5 Hz, the target and background

slip are equal, but learning went in the direction predicted by the target, suggesting

that something other than tuning to lower image motion velocity is influencing the

target’s effect on putative instructive signals and learning.

2.3.3 Behaviorally-relevant and foveal/parafoveal stimuli

The animals in the experiments were trained to track the visual target, and rewarded

for such behavior. Therefore, the visual target was the behaviorally-relevant stimulus,

and probably the focus of the animal’s attention. Recent evidence demonstrate that

climbing fiber responses in afoveate rabbits during the presence of transparent slip are

associated with the retinal slip of the visual stimulus that triggers an eye

movement[35]. Therefore, the effect of the target on learning may be mediated

through the attentional gating of instructive signals carried by the climbing fibers.

In animals with a fovea, the visual stimulus that is the object of the animal’s attention

is often placed on the fovea. Therefore, it is difficult to dissociate whether attention

gates the climbing fiber response to the target, or whether the target exerts its

influence on the climbing fiber response because floccular climbing fibers place

greater weight on foveal image motion. There is evidence that suggest image motion

38

on the fovea plays critical importance in driving climbing fibers. A major input to the

dorsal cap of the inferior olive, the origin of floccular climbing fibers, is the nucleus of

the optic tract (NOT)[36]. Neurons in NOT in monkeys have been divided into two

classes based on their response properties[37, 38]. One class of neurons responds

during ipsiversive tracking of a visual target when presented alone or in the presence

of a stationary background. Another class of neurons responds during contraversive

tracking of a visual target but only when the target motion occurs in the presence of a

background. It appears that these neurons, respond to either retinal slip associated

with the target or background, respectively. Thus, the influence of the target on

climbing fiber signals may be reflected in the response properties of the neurons that

innervate the source of climbing fibers, the inferior olive.

The function of the VOR, based on studies in phylogenetics and VOR kinematics, is to

stabilize full-field images, paying no specific attention to stabilizing the visual

stimulus to one specific area of the retina. Based on this understanding of VOR

function, it may be predicted that full-field image motion would play a more important

role in guiding VOR learning[39, 40]. However, our results demonstrate that the

brain does not just care about net motion energy on the retina as what might be

predicted. Rather factors related to the target must influence the putative neural

instructive signals that guide learning.

2.3.4 Effects of target and background motion on putative neural instructive

signals for motor learning in the VOR

The combinations of target and background motion during visual-vestibular stimuli

used to induce VOR adaptation elicited many different combinations of Purkinje cell

and climbing fiber instructive signals. Our data demonstrated two main results: first,

climbing fibers in the floccular complex encode information about the target and

background, with the climbing fiber response reflecting a complex, non-linear

interaction between the two visual stimuli. Second, the ability to break the previously

observed covariation between climbing fiber and Purkinje cell signals[34] demonstrate

39

that to some degree, inputs to Purkinje cells in the floccular complex, can

independently drive simple and complex spike activity.

2.3.5 Climbing fibers do not encode net image motion on the retina

The earliest studies of floccular climbing fiber responses used motion of a single,

large-field visual stimulus to induce coherent image motion on the retina[5, 41].

These studies found that floccular climbing fibers increased their firing during

contraversive retinal image motion, and were tuned to low retinal slip velocities.

More recently, studies in primates have demonstrated that motion of a small visual

target was sufficient to drive floccular climbing fibers, although the climbing fiber

response were typically smaller than when a large-field stimulus was nused[30].

Classically, floccular climbing fibers have been thought to carry only visual

information. However, more recent recordings of floccular climbing fibers

demonstrated a population of neurons that modulate in the light during performance of

the optokinetic reflex (OKR) and during VOR in complete darkness, suggesting that

these climbing fibers carry a non-visual, vestibular signal[42]. In the study, during

performance of the OKR, the climbing fibers increased their firing during

contraversive motion of the optokinetic drum, and decreased their firing during

ipsiversive motion of the drum, consistent with the earlier findings studies that

floccular climbing fibers are driven by contraversive image motion, and inhibited by

ipsiversive image motion on the retina. The same climbing fibers during performance

of the VOR in complete darkness increased their firing during contraversive head

movements, and decreased their firing during ipsiversive head movements. The

climbing fiber response was also tested during performance of the VOR in the light

(x1 stimulus). In the light, contraversive head rotations (which drive the climbing

fiber response) result in ipsilateral image motion (which inhibits the climbing fiber

response), so if the climbing fibers response were to reflect both its visual and

vestibular inputs, then the climbing fibers should not respond during VOR in the light.

However, the same climbing fibers that carry non-visual signals are strongly

modulated during VOR in the light, in the direction predicted by image motion. These

40

results demonstrated two important qualities of floccular climbing fibers. First, there

is a non-visual signal in floccular climbing fibers. And second, there is a complex,

non-linear interaction between the climbing fiber inputs. For example, it may be

possible that the presence of a visual signal gates the effectiveness of the vestibular

input at driving climbing fiber activity. If climbing fiber signals were an error signal

guiding motor learning in the VOR, one may expect that the climbing fiber

modulation during VOR in the dark to induce VOR learning. However, a vestibular

stimulus in the absence of any visual stimuli did not induce any consistent changes in

VOR gain [43].

Another challenge to earlier findings that floccular climbing fibers carry only visual

signals related to image motion is a study by Frens, et al. In their study, they recorded

floccular climbing fibers during transparent motion[35]. Transparent motion occurs

when there are multiple motions in the same part of the visual space, and can be

created by two overlapping visual stimuli moving in opposite direction. When rabbits

are exposed to transparent motion stimuli, the rabbits follow one of the two visual

stimuli. The induced eye movements would create multiple image motions on the

retina. Frens, et al. found that responses in the floccular climbing fibers were best

correlated to the retinal image slip of the visual stimulus that was being followed,

suggesting that climbing fibers do not encode net image motion on the retina, but

rather the sensory consequence of a movement, or a “performance error.” However,

there were several confounds that may dispute this conclusion. First, since climbing

fibers were correlated to eye movements in the study, it may be possible that climbing

fiber signals contained a motor signal. Indeed follow up studies using optokinetic

white noise stimuli demonstrated that climbing fiber responses were better correlated

to the eye movements, than to retinal slip[44]. Also, since the visual stimulus that

elicited an eye movement is most likely to be the behaviorally-relevant or attended

stimulus, it may be possible that attention gates the climbing fiber response. And last,

since the eye movements elicited by a visual stimulus would decrease the retinal slip

velocity of that stimulus relative to visual stimuli that were stationary or moving in the

41

opposite direction, it may be possible that climbing fibers respond best to the image

motion moving the slowest on the retina.

Our current study also addressed how floccular climbing fibers respond during

simultaneous presentation of multiple, and at times, oppositely directed image motion

on the retina. In our study, the visual target was the behaviorally-relevant stimulus, in

that the animals were trained to track the target for a juice reward. A key difference

between our findings and those observed in the Frens’ study is that in the Fren’s study,

the climbing fiber response only reflected the image motion resulting from the visual

stimulus eliciting the eye movement. In our study, climbing fiber responses were

affected not only by the stimulus driving eye movements—the target, but also by the

large background, especially when background elicited retinal slip in the opposite

direction of target slip.

Another key difference in our study was that we used rhesus monkeys instead of

rabbits. Rhesus monkeys, unlike rabbits, are foveated animals. It may be possible that

visual stimuli placed on the fovea are given greater weight at driving climbing fiber

responses. Indeed, recordings from neurons in the nucleus of the optic tract (NOT),

the main input neurons to the dorsal cap of the inferior olive which give rise to

floccular climbing fibers, demonstrated two classes of neurons in near equal numbers:

neurons that were sensitive to motion of a foveal target, and those sensitive to motion

of a large visual background [37, 38]. It may be possible that oppositely directed

motion of a target and background would tend to cancel the response at the level of the

inferior olive. However, in our current results, there was a non-linear interaction

between the target and background at driving climbing fiber responses. During

training stimuli that elicited smaller target slip velocities (due to more effective

tracking eye movements), the effectiveness of an oppositely directed background at

reducing the climbing fiber responses was greater. One possible explanation may be

that the NOT neurons encoding target motion may be tuned to higher target slip

velocities, so as target slip velocities increase, the target played a greater role relative

42

to the background at driving climbing fiber responses. The precise velocity tuning of

these two distinct populations of NOT neurons remains unknown.

2.3.6 Modulation of Purkinje cell simple spikes is independent of climbing fiber

signals

In a previous study, despite blocking vestibular primary afferent signals to the

ipsilateral uvula-nodulus by unilateral labryinthectomy, simple spike modulation in

ipsilateral Purkinje cells was observed during head rotations, suggesting that climbing

fiber signals originating from the contralateral inferior olive modulated the simple

spike response[34]. In our study, we were able to remove the climbing fiber response

while maintaining the simple spike modulation, suggesting that at least in the

flocculus, a component of the simple spike modulation is independent of climbing

fiber signals. In our study, we recorded the simple and complex spike response of a

single neuron to many different visual-vestibular stimuli, and found that within a

single neuron, climbing fiber response may be altered while simple spike responses

remained constant. Thus, our data suggest that the modulation of simple spikes in a

particular Purkinje cell may be independent of modulation of the climbing fiber that

projects to that same Purkinje cell. It may be possible that climbing fibers activating

adjacent Purkinje cells or cerebellar interneurons drive the simple spike modulation in

the recorded Purkinje cell. However, our data across animals do not support this

argument. Across the three animals used in our experiments, conflicting background

motion uniformly reduced the climbing fiber response relative to consistent

background motion, so it is reasonable to assume this manipulation would have a same

effect on neighboring climbing fiber responses in each of the animals. If these

neighboring climbing fibers modulate Purkinje cell simple spikes, we would expect a

conflicting background to have the same effect on simple spike modulation in each of

the three animals. However, there were different effects of the conflicting background

on the Purkinje cell simple spike responses. In one animal, the simple spike response

was the same as during consistent background motion (Monkey E), in another monkey

the simple spike response was reduced (Monkey C), and in the last animal the simple

43

spike response was enhanced (Monkey L). Therefore, the different effects of the

conflicting background on simple spike modulation are unlikely to be the result of

neighboring climbing fiber responses. Rather, the different Purkinje cell simple spike

responses were correlated to the different effects of the conflicting background on

tracking eye movements. Thus, the eye movement inputs to the Purkinje cells and the

retinal slip inputs to the climbing fibers can effectively drive signals in each

population of neurons independently of each other.

44

2.4 Materials and methods

General Procedures

Experiments were conducted on three male rhesus monkeys. Animals were trained to

perform a visual fixation task to obtain liquid reinforcement. Previously described

surgical procedures were used to implant orthopedic plates in the skull for restraining

the head[22, 45], and to implant a coil of wire in one eye for measuring vertical and

horizontal eye position[46]. A second surgical procedure was conducted to

stereotaxically place a recording cylinder for single-unit recording. During

experiments, each monkey sat in a specially designed primate chair to which his

implanted head holder was secured. Vestibular stimuli were delivered using a servo-

controlled turntable (Ideal Aerosmith) that rotated the animal, the primate chair, and a

set of magnetic coils (CNC Engineering) together about an earth-vertical axis. Visual

motion stimuli were provided by a visual “target” subtending 0.5˚ of visual angle,

which the animal was rewarded for tracking, and a visual “background” consisting of

a high contrast, black and white checkerboard pattern (20˚ x 30˚). The visual stimuli

were reflected off mirror galvanometers onto the back of a tangent screen 114 cm in

front of the eyes.

Behavioral experiments

Motor learning in the VOR was induced by presenting combined visual-vestibular

stimuli for one hour (Monkey E) or two hours (Monkey L). VOR performance was

tested before, after and at 15 minute intervals (Monkey E) or 30 minute intervals

(Monkey L) during training, by delivering the vestibular stimulus in total darkness.

The vestibular stimulus used to measure the VOR and induce learning had a

sinusoidal velocity profile (0.5 Hz, peak velocity ±10˚/s or in a few cases, where

noted, ±20˚/s). The visual-vestibular training stimuli are described by the eye velocity

gain (relative to head movement) required to stabilize the image of the target (T) and

45

the background (BG) on the retina. During training stimuli with a x0T, the target

moved exactly with the head, so an eye movement gain of zero would be required to

stabilize the target on the retina. During training stimuli with a x2T, the visual target

moved at the same speed as the head but 180˚ out of phase with the head, thus, eye

movements would need to be twice as large as the head movements (gain of 2) to

stabilize the target on the retina. During training stimuli with a x0.5T or x1.5T, the

target moved in phase or 180˚ out of phase with the head, respectively at one-half the

head speed. During training stimuli with x1T, the visual target was earth-stationary.

With the exception of two experiments conducted in Monkey L (x0T only and x2T

only), the visual stimulus was comprised of visual background and target motions. In

some experiments, the motion of the target and background were the same

(x0T/x0BG, x0.5T/x0.5BG, x1T/x1BG, x1.5T/x1.5BG, x2T/x2BG). In the remaining

experiments, there was non-coherent motion of the target and background. During

x0T/x2BG, x0.5T/x1.5BG, x1.5T/x0.5BG and x2T/x0BG stimuli, the visual

background moved at the same speed but 180˚ out of phase with target motion.

During the x0T/x1BG, x0.5T/x1BG, x1.5T/x1BG and x2T/x1BG stimuli, the visual

background was earth-stationary during target motion. During x0T/x0.5BG and

x2T/x1.5 BG stimuli, the background moved at one-half the speed and in-phase with

target motion. During the x0.5T/x0BG and x1.5T/x2BG stimuli, the background

moved at twice the speed and in phase with target motion. During the x1T/x0BG and

x1T/x2BG stimuli, the visual target was earth-stationary, while the background mo-

ved at the same speed and in phase or 180˚ out of phase with the head, respectively.

Experiments were separated by at least 24 hours to allow the gain of the VOR to

readapt to its normal value before the next experiment. There were a minimum of

three replications of the behavioral experiments for each training stimulus.

46

Electrophysiology

Tungsten electrodes (FHC, Microprobe) were used to make extracellular recordings

from Purkinje cells in the flocculus/ventral paraflocculus of the cerebellum. After a

Purkinje cell was isolated, its sensitivity to eye velocity and head velocity were first

measured by recording its responses during (1) smooth pursuit eye movements evoked

by horizontal motion of the visual target with a sinusoidal velocity profile at a

frequency of 0.5 Hz and a peak velocity of 20˚/s or greater and (2) as the monkey

canceled his VOR by tracking a visual target that moved exactly with sinusoidal head

rotation about an earth vertical axis at 0.5 Hz and at a peak velocity of 20˚/s or greater.

The present work focuses on horizontal-gaze velocity Purkinje cells (HGVPs), a well

studied class of Purkinje cells in the floccular complex. Purkinje cells were classified

as HGVPs if (1) during horizontal smooth pursuit eye movements, simple spike firing

rate was modulated by at least ±0.3 sp/s per ˚/s, and there was a phase difference of

less than 45 degrees between peak firing rate and peak ipsiversive eye velocity; (2)

during cancellation of the VOR, simple spike firing rate was modulated by at least

±0.3 sp/s per ˚/s and the phase difference between peak firing rate and peak ipsiversive

head velocity was less than 45 degrees [6, 16]. [6, 16]. A total of 76 HGVPs were

recorded. Complex spikes were well isolated in 58/76 HGVPs. In the 18 recorded

neurons without complex spike activity, the neurons were deemed Purkinje cells

because of their simple spike wave form, their irregular simple spike firing rate, the

ability to record the cells through several hundred micrometers of cerebellum, and in

some cases, their characteristic injury discharge at the end of recording

We compared the instructive signals carried by the same neuron during several

different training stimuli used to induce motor learning in the VOR. Each training

stimulus was presented for 60-90 seconds. Recordings were made when the gain of the

VOR was at baseline, and the training stimuli were not presented long enough to

induce measurable changes in VOR performance as measured in the dark.

47

Data analysis

Voltages related to the position and velocity of eye, head and visual stimulus were

recorded during the experiment at 500 Hz/channel. Eye velocity records were edited

to remove the rapid deflections caused by saccades. The data were then analyzed by

aligning stimulus cycles on head or target velocity, and averaging. Most averages

contained 10 or more cycles, and analyses were limited to cycles for which gaze

position was within 15 degrees of straight-ahead gaze. Average eye and head velocity

traces were subjected to a sines fit. The gain of the VOR was calculated as the ratio of

peak eye to head velocity derived from the fitted sinusoidal functions.

To calculate horizontal gaze velocity, the raw eye and head velocity traces were

summed for each stimulus cycle. In these calculations, the rapid deflections caused

saccadic eye movements were not removed from the records. The gaze velocity

records were aligned to peak head velocity and subjected to a sines fit. The amplitude

of the gaze velocity was the amplitude of the fitted sinusoidal function. Gaze

velocities were assigned positive and negative values according to its relative

relationship to head movements. The phase of gaze velocity was defined as the

timing of the occurrence of peak gaze velocity relative to head velocity. Positive

phase values for gaze velocity indicated a phase lead, where the occurrence of peak

gaze velocity preceded peak head velocity.

To calculate retinal slip of the target, target velocity was calculated from an offline

differentiation of the recorded target position trace. Retinal slip of the target was

calculated as the difference in horizontal gaze velocity and target velocity for each

individual stimulus cycle. The retinal slip records were aligned to peak head velocity

and subjected to a sines fit. The amplitude of the target slip velocity was the

amplitude of the fitted sinusoidal function. The phase of target slip velocity was

defined as the timing of the occurrence of peak target slip velocity relative to head

48

velocity. Positive phase values for retinal slip indicated a phase lead, where the

occurrence of peak target slip preceded peak head velocity.

Since the voltage signal conveying the target command was used to control motion of

the visual background, the retinal slip of the background was calculated using the

recorded target position trace. For example, if the background moved 180˚ out-of-

phase but at the same speed as the target (e.g. during x0T/x2BG), the values

pertaining to background position were equal to values associated with target position

multiplied by (-1). If the background moved in-phase but at one-half the speed of the

target, the values pertaining to background position were equal to values associated

with target position multiplied by (0.5). Once the background position was

calculated, the amplitude and phase of background slip were derived using the same

method as calculating amplitude and phase of target slip.

The component of the gaze velocity, background slip velocity, and target slip velocity

aligned with peak head velocity was calculated by multiplying the amplitude of the

response with cosine of the phase, and these values were used for statistical analysis.

The simple-spike activity of Purkinje cells was detected with a hardware window

discriminator, and the times of the resulting pulses were recorded to the nearest

10 µsec. In addition, unit activity was sampled at 50 kHz, and complex spikes were

discriminated using off-line spike sorting with time and amplitude windows or

template matching algorithms (Spike2, Cambridge Electronic Design, Cambridge,

UK). In addition, the occurrence of each complex spike was confirmed by visual

inspection of the raw traces.

Data analysis was performed in Matlab and Excel. The simple spike data were

analyzed after the experiment by aligning the records on head velocity or visual

stimulus position. The amplitude of firing rate modulation and phase of the simple

spike responses relative to peak contraversive head velocity were estimated as the

49

amplitude and phase of the fundamental components provided by Fourier analysis of

the averages.

Because some stimuli drive climbing fiber activity into firing rate cutoff, the climbing

fiber responses were not always well described by a sinusoid. Therefore, to quantify

climbing fiber responses during training stimuli, complex spike data were analyzed

using a vector analysis. Stimulus cycles were aligned on head velocity and averaged.

The stimulus cycle was divided into 1000 equal bins. Each time bin was represented

as a vector with the magnitude of the vector equal to the average firing rate in that bin,

and the phase determined by the phase of the bin relative to peak ipsiversive head

velocity. The phase and amplitude of the climbing fiber response were calculated as

the phase and one-half the amplitude of the vector sum.

The component of the climbing fiber response or simple spike response aligned with

peak head velocity was calculated by multiplying the amplitude of the response with

cosine of the phase, and these values were used for statistical analysis. The

significance of each neural response was determined by performing the vector analysis

on each cycle of head movement during a given training stimulus. Significance was

tested using a one-sample t-test.

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Chapter 3 Elimination of climbing fiber instructive signals during motor learning

3.1 Introduction

To understand the algorithm a neural circuit uses to learn, one must determine how its

patterns of activity during the induction of learning are translated into the cellular

changes that encode memory. The cerebellum, which supports motor learning, is one

brain region for which there is a well-developed theory about the neural events that

induce plasticity during learning. The dominant theory over the last several decades

has postulated that the climbing fibers provide the neural instructive signals that guide

cerebellum-dependent learning[2, 4, 12]. In support of the climbing fiber hypothesis,

in vivo recordings have shown that climbing fiber activity signals errors during a

number of different motor learning paradigms[6, 29, 47, 48]. Moreover, previous

studies have documented the sufficiency of climbing fiber signals to induce learning—

training with paired electrical stimulation of the climbing fibers and a conditioned

stimulus (CS) can induce a conditioned response (CR) when the CS is subsequently

presented alone[49].

The classic Marr-Albus-Ito theory attributed cerebellum-dependent learning to

climbing fiber-triggered plasticity at the parallel fiber-to-Purkinje cell synapses in the

cerebellar cortex[3, 12]. More recent evidence suggests that multiple, distributed

mechanisms contribute to cerebellum-dependent learning[26, 50-54]. Nevertheless,

climbing fiber-triggered plasticity is still considered central to cerebellum-dependent

learning[50, 54-56], since the climbing fibers are positioned to control multiple

plasticity mechanisms in the cerebellum and related circuitry. At the parallel fiber-to-

Purkinje cell synapses, an increase or decrease in climbing fiber activity relative to its

baseline activity could provide an instructive signal that triggers long-term depression

(LTD) or long-term potentiation (LTP), respectively[14, 57]. At other classes of

synapses in the cerebellar cortex, climbing fiber activation controls several additional

plasticity mechanisms[23, 33, 58-60]. Moreover, changes in the deep cerebellar nuclei

(DCN) or vestibular nuclei (VN) during cerebellum-dependent learning are widely

51

viewed as secondary to or dependent upon climbing fiber-triggered changes in the

cerebellar cortex[24, 50, 53, 54]. Thus, neural instructive signals in the climbing

fibers could, either directly or indirectly, orchestrate the induction of multiple forms of

plasticity in the cerebellum and related circuitry. The central question addressed in

this chapter is whether these climbing fiber-controlled plasticity mechanisms are

necessary for motor learning, or whether plasticity mechanisms controlled by other

neural instructive signals could independently support learning.

Previous tests of the necessity of the climbing fibers for motor learning have been

inconclusive. Lesion or pharmacological inactivation of the source of the climbing

fibers, the inferior olive, abolishes cerebellum-dependent learning[61, 62]. However,

such manipulations abolish spontaneous neural activity in the climbing fibers as well

as the task-related signals carried by changes in firing rate above and below the

spontaneous level of activity. The elimination of spontaneous activity has the effect of

producing abnormal neural activity at multiple sites in the cerebellar circuit[63, 64].

Thus, the inability to learn after such manipulations cannot be directly attributed to the

loss of the instructive signals in the climbing fibers, but could simply reflect the gross

cerebellar dysfunction associated with disrupted basal activity. To avoid this

confound, we developed a behavioral approach to selectively eliminate the instructive

signals in the climbing fibers without affecting their baseline level of activity.

The paradigm we used was the adaptive modification of the vestibulo-ocular reflex

(VOR) by motor learning. Signaling in the VOR circuit is understood at a level that

enabled us to design training stimuli that would abolish instructive signals in the

climbing fibers, while leaving intact most other aspects of the training conditions

known to induce VOR learning. The VOR is a reflexive eye movement that functions

to stabilize images on the retina by generating eye movements in the opposite

direction from head motion. If the VOR fails to stabilize images during head

movements, motor learning can adjust the amplitude, or gain, of the VOR (i.e., the

ratio of eye velocity to head velocity) to restore image stability. This form of motor

52

learning requires the floccular complex of the cerebellum, comprising the cerebellar

flocculus and ventral paraflocculus. Lesions of the floccular complex abolish the

adaptive modification of the VOR through motor learning without affecting its

baseline performance[65, 66], whereas lesions of other regions of the cerebellum

carrying vestibular, visual, or oculomotor signals do not affect motor learning in the

VOR[67-69].

In the laboratory, motor learning in the VOR is typically induced by pairing head

movements with motion of a single, large, coherently moving visual stimulus (Fig.

3.1b). If the visual stimulus moves exactly with the head, a decrease in the gain of the

VOR is induced. Such training stimuli are called “x0”, because the VOR gain

required to stabilize images on the retina is zero. If, instead, the visual stimulus moves

exactly opposite to head motion, an increase in VOR gain is induced. Such training

stimuli are called “x2” because the ideal VOR gain would be 2 (eye velocity equal to

twice head velocity).

One line of evidence that instructive signals in the climbing fibers control motor

learning in the VOR has been that the climbing fibers carry information about the

53

required direction of learning during x0 and x2 training. Climbing fibers in the

floccular complex are driven by contraversive image motion and inhibited by

ipsiversive image motion[5, 41]. During x0 training, contraversive image motion

occurs during contraversive head motion, whereas during x2 training, contraversive

image motion occurs during ipsiversive head motion. Thus, the timing of the peak

climbing fiber activity relative to head motion carries information about whether the

VOR gain needs to increase or decrease[5, 6, 13]. Based on the known physiology of

the circuit, it has been proposed that these differently timed climbing fiber responses

induce LTD and/or LTP in the appropriate vestibular parallel fiber-to-Purkinje cell

synapses to support the observed changes in VOR gain[2, 4, 12, 26, 70] (Fig. 3.1).

During head rotations in the dark, when no visual cues are available to guide motor

learning in the VOR, and no consistent changes in VOR gain are induced, the same

climbing fibers have little or no response, but simply fire at their spontaneous rate[43,

71].

Thus, with standard training conditions, the climbing fibers carry information about

whether the VOR gain needs to be altered, and they appear to be capable of

implementing appropriate changes in the VOR circuit to accomplish the required

behavioral changes. In the present study, we compared the climbing fiber responses

during standard x0 and x2 training stimuli, with those present during variations of

these stimuli designed to eliminate the instructive signals in the climbing fibers. Our

results suggest that learning can be induced in the absence of instructive signals in the

climbing fibers and that plasticity mechanisms controlled by other neural instructive

signals make a significant and independent contribution to motor learning.

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3.2 Results

We analyzed the neural instructive signals available in the VOR circuit during a range

of training paradigms that induce motor learning in the VOR.

In the floccular complex of two rhesus monkeys, 102 Purkinje cells with task-related

activity (head and/or eye movement sensitivity) were recorded. Our analysis focused

on 58 of these Purkinje cells, which were identified as horizontal gaze velocity

Purkinje cells (HGVPs), a subclass of neurons that have been implicated in VOR

learning[15, 16]. Since spikes in a climbing fiber reliably trigger complex spikes in its

Purkinje cell targets in a ‘one-to-one’ manner, complex spike activity in a Purkinje

cell was used as a measure of activity in its climbing fiber input[10], and is referred to

as a “climbing fiber response”. Complex spikes were well isolated in 48/58 HGVPs

and 20/44 non-HGVPs. We also analyzed the simple spike responses of the Purkinje

cells to each training stimulus. In each individual neuron recorded, the responses to a

number of different visual-vestibular training stimuli were compared.

A similar set of behavioral and electrophysiology experiments were conducted on a

third animal, Monkey C, whose behavior and electrophysiology data is presented in

Chapter 2. However, the x0T/x2BG training condition which eliminated climbing

fiber responses in Monkey L and Monkey E (see Results below), elicited a statistically

significant climbing fiber response in Monkey C (see Appendix for individual

responses to x0T/x2BG; p < 0.001, one sample t-test), and therefore, we were unable

to test the necessity of climbing fiber instructive signals to motor learning in this

animal. Therefore, we excluded Monkey C’s dataset from this chapter.

3.2.1 Standard training stimuli elicit instructive signals in climbing fibers

Consistent with previous studies from a number of labs, the population of climbing

fibers we recorded discriminated between a x0 versus a x2 training stimulus[5, 6, 13],

and thus carried information about whether the VOR gain needed to increase or

decrease. During standard, x0 training, with coherent motion of a visual target (T;

55

subtending 0.5° of visual angle) and a visual background (BG; 20° x 30°), both

moving exactly with the head (0.5 Hz, ±10°/s), climbing fiber firing peaked during

contraversive head movement (x0T/x0BG training, Fig. 3.2); whereas during standard,

x2 training, climbing fiber firing peaked during ipsiversive head movement

(x2T/x2BG, Fig. 3.2).

The climbing fiber responses during the x0T/x0BG and x2T/x2BG training stimuli

reflected both an increased probability of firing during the “preferred” half-cycle of

the stimulus, and a decreased probability of firing during the “non-preferred” half-

cycle of the stimulus, relative to the baseline, spontaneous activity measured in the

absence of head movement or visual stimulus movement (Fig. 3.2b). Across the

population of climbing fibers, the responses were remarkably uniform (gray traces,

Fig. 3.2b). During the “preferred” half-cycle, a large fraction of the climbing fibers

fired with a probability two standard deviations (SD) above baseline, and almost no

climbing fibers fired with a probability 2 SD below baseline (Fig. 3.2c). Likewise,

during the “non-preferred” half-cycle, a large fraction of the climbing fibers fired with

a probability 2 SD below baseline, and almost no cells fired with a probability 2 SD

above baseline (Fig. 3.2c).

56

The stimulus-driven changes in the probability of climbing fiber firing were not

restricted to a particular time during the stimulus. Rather, throughout the stimulus

cycle, the probability of firing was 2 SD above or below baseline in the majority of

cells, and the probability of firing smoothly transitioned from low to high during the

stimulus cycle, reflecting the sinusoidal velocity profile of the training stimulus.

Therefore, each climbing fiber response was summarized by the amplitude of the

overall firing rate modulation during the stimulus cycle, and the phase of the peak

57

firing relative to head movement, calculated using a vector analysis (Fig. 3.2d; see

Methods).

The significance of each climbing fiber response was calculated by analyzing the

cycle-by-cycle variability. During the standard training stimuli, x0T/x0BG and

x2T/x2BG, almost all climbing fibers had responses significantly different from zero

(42/42 climbing fibers for x2T/x2BG, 45/48 climbing fibers for x0T/x0BG, Fig. 3.2d,

bold symbols), and, for each stimulus, the timing (phase) of peak firing relative to the

head movement was similar in all neurons with significant responses (within 70°).

Thus, the population response of the climbing fibers was significantly different from

zero for both x0T/x0BG and x2T/x2BG training stimuli (p<0.05, one sample t-test,

Table 3.1). Moreover, the population responses to the x0T/x0BG and x2T/x2BG

stimuli were significantly different from each other, since the phase of peak firing

relative to head movement was approximately 180° opposite for the two stimuli (Fig.

3.2d, Table 3.1). These climbing fiber responses were similar to those reported

previously in primates and other species[6, 13, 72].

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3.2.2 Novel training stimuli eliminate instructive signals in climbing fibers

To eliminate climbing fiber responses during VOR training, and thereby test their

necessity for motor learning, we paired head movements with oppositely directed

motion of two visual stimuli. The origin of floccular climbing fibers is the

contralateral dorsal cap of the inferior olive, which in turn receives its major input

from the ipsilateral nucleus of the optic tract (NOT)[73]. Neurons in the NOT

increase their firing rate during ipsiversive image motion, and decrease their firing rate

during contraversive image motion. Within this population are two distinct subtypes

of neurons, found in approximately equal numbers: those sensitive to motion of a

foveal target, and those sensitive to motion of a large visual background[37, 38].

Therefore, we expected that the effects of oppositely directed motion of the target and

background might tend to cancel at the level of the inferior olive, and this was

confirmed by our recordings from the climbing fibers. When head movements were

paired with target motion plus oppositely directed background motion (x0T/x2BG,

Fig. 3.2; x2T/x0BG, Fig. 3.3), the responses of the climbing fibers were greatly

reduced compared with the responses in the same climbing fibers when the target and

background moved together (x0T/x0BG, x2T/x2BG, Fig. 3.2).

The reduction in climbing fiber response was greater for the x0T/x2BG training

stimulus than the x2T/x0BG stimulus. At the population level, the climbing fiber

response to the x2T/x0BG stimulus was significantly different from zero (one sample

t-test, Fig. 3.3, Table 3.1), therefore this stimulus did not provide a good test of the

necessity of climbing fiber instructive signals for motor learning. In contrast, the

population response to the x0T/x2BG stimulus was not significantly different from

zero (P > 0.05, one sample t-test; Table 3.1). Therefore, we conducted additional

analyses to evaluate whether the climbing fiber response was truly eliminated during

the x0T/x2BG stimulus.

59

When the cycle-by-cycle variability of individual climbing fiber responses was

considered, only 4/15 (Monkey L) and 1/30 (Monkey E) climbing fibers had

significant responses to the x0T/x2BG stimulus (Fig. 3.2d, x0T/x2BG, bold symbols).

Moreover, the timing of peak firing was not consistent in these five climbing fibers

with significant responses. Three climbing fibers increased their firing during

ipsiversive head movement, as observed during x2T/x2BG training, and two climbing

fibers increased their firing during contraversive head movement, as observed during

x0T/x0BG training.

We considered the possibility that climbing fiber responses may be restricted to a very

specific time in the stimulus cycle, but found no evidence for a temporally specific

climbing fiber response to the x0T/x2BG training stimulus. We compared the

probability of climbing fiber firing during each 200-ms segments of the x0T/x2BG

60

stimulus cycle with spontaneous activity and with spike trains obtained by randomly

shuffling the interspike intervals measured during the x0T/x2BG training stimulus to

remove any potential signal (shuffled, spontaneous, Fig. 3.2). Because of natural

variability in the climbing fiber interspike intervals, when spontaneous or shuffled

activity was averaged across 2000-ms “stimulus cycles” (see Methods), there was, in

each 200-ms time bin, a small percentage of the climbing fibers firing with a

probability 2 SD above or below the mean (controls, Fig. 3.2). However, in each time

bin, a similar percentage of climbing fiber had increased versus decreased firing,

indicating there was no signal carried by the population. In contrast, at any given time

point during the x0T/x0BG and x2T/x2BG training stimuli, a larger percentage of

climbing fibers fired 2 SD above or below baseline, and the percentage of climbing

fibers with increased versus decreased firing was highly asymmetric, reflecting the

signals carried by the population of climbing fibers during these standard stimuli.

During the x0T/x2BG training stimulus, climbing fiber activity was indistinguishable

from spontaneous climbing fiber activity and shuffled spike trains. Furthermore,

inspection of the raw climbing fiber spike trains revealed no evidence for a

temporally-specific response to the x0T/x2BG training stimulus on a finer timescale or

within a subset of trials.

Although the conflicting background motion during the x0T/x2BG stimulus

eliminated the climbing fiber responses, it did not affect the overall average firing rate.

On average, the overall firing rate

in the climbing fibers during the

x0T/x2BG training stimulus was

the same as the average firing

rate during spontaneous activity,

and during x0T/x0BG and

x2T/x2BG training stimuli (P >

0.05, ANOVA, Fig. 3.4). Hence,

the x0T/x2BG stimulus achieved

61

selective elimination of instructive signals in the climbing fibers without affecting the

baseline firing rate, as required to test the necessity of the instructive signals in the

climbing fibers for learning.

3.2.3 Instructive signals carried by Purkinje cell simple spikes

One candidate instructive signal that was previously proposed to guide motor learning

in the VOR is the simple spike output of the Purkinje cells, and during the novel

x0T/x2BG stimulus, the Purkinje cell simple spikes carried robust signals that could

potentially guide the induction of learning. As with the climbing fibers, the timing of

peak simple spike activity relative to the head movement discriminated between the

standard, x0 and x2 training stimuli when the target and background moved together

(x0T/x0BG, x2T/x2BG, Fig. 3.5). Unlike the climbing fibers, the Purkinje cell simple

spike activity also carried large, potentially useful instructive signals during

x0T/x2BG training (Fig. 3.5). The simple spike activity of the Purkinje cells encodes

both the vestibular input and the eye movements the monkey makes to track the visual

target[16]. The vestibular stimulus was the same across the experiments. The

tracking eye movements were also similar during x0T/x0BG and x0T/x2BG training,

because motion of the visual target was the same. Therefore, the Purkinje cell simple

spike responses during x0T/x2BG training were indistinguishable from those during

x0T/x0BG training (P > 0.19, paired t-test; Fig. 3.5, Table 3.1), thus providing a way

to assess the potential contribution of Purkinje cell simple spikes to the induction of

learning in the absence of instructive signals in the climbing fiber.

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3.2.4 Learning in the absence of instructive signals in the climbing fiber

Despite the absence of instructive signals in the climbing fibers, the x0T/x2BG

training stimulus induced consistent motor learning. In the two animals used for

neural recordings, motor learning in the VOR was induced by presenting one of the

training stimuli for one hour (Monkey E) or two hours (Monkey L). VOR learning

was assessed by comparing the eye movement response to head movements in

complete darkness before and after training. The standard, x0T/x0BG and x2T/x2BG

training stimuli induced decreases and increases in VOR gain, respectively. The

x0T/x2BG training stimulus also induced a significant decrease in VOR gain (P <

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0.001, one sample t-test; 4 of 4 training sessions in Monkey L; 5 of 5 training sessions

in Monkey E; Fig. 3.6).

Comparison of the x0T/x2BG stimulus with other novel stimuli revealed that training

stimuli that elicited similar instructive signals in the climbing fibers could induce

opposite changes in the behavior, which were correlated with the simple spike

responses. In Monkey L, the climbing fiber response to the x2T/x0BG stimulus was

not statistically different from the response during the x0T/x2BG stimulus (P > 0.05,

paired t-test; Table 3.1). Nevertheless, in 3 of 3 training sessions, x2T/x0BG training

induced a learned increase in VOR gain in Monkey L (Fig. 3.3), in contrast to the

decrease in VOR gain induced by x0T/x2BG training in 4 of 4 training sessions in the

same monkey (Fig. 3.6). The simple spike response elicited by the x2T/x0BG

stimulus was similar to that elicited by the x2T/x2BG stimulus (P > 0.05, paired t-test;

Table 3.1, Fig. 3.3 and Fig. 3.6), and therefore may have contributed to the increase in

VOR gain observed in each case.

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In Monkey E, the climbing fiber response to the x2T/x0BG training stimulus was

significantly different from the response to the x0T/x2BG stimulus (P = 0.002, one

sample t-test; Table 3.1). Therefore, we designed additional training stimuli for this

monkey. Three training stimuli— x0T/x2BG, plus x0.5T/x1BG and x1.5T/x0.5BG

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(see Methods for detailed description of training paradigms), each elicited no

significant climbing fiber response (Fig. 3.6a, see Appendix for individual neural

responses). However, the target motion was different during each of these three

training stimuli, which elicited different eye movements and, therefore, different

Purkinje cell simple spike responses (Fig. 3.6b). This provided an opportunity to

assess the ability of different simple spike responses to induce learning. Although the

climbing fiber responses to these three stimuli were the same, the learned changes in

VOR gain they induced were different (Fig. 3.6c, Table 3.1). Moreover, the changes

in VOR gain were correlated with the simple spike responses during training; for these

three stimuli, the biggest decreases in VOR gain occurred when the Purkinje cell

simple spike responses were most similar to those during the standard, x0T/x0BG

training stimulus.

3.2.5 Learning in the absence of instructive signals in the simple spikes

The observation of motor learning in the absence of instructive signals in the climbing

fibers does not exclude a contribution of climbing fiber-triggered plasticity

mechanisms to VOR learning. An additional training stimulus, x1T/x0BG, was used

to isolate the climbing fiber contribution by eliminating the putative instructive signals

in the Purkinje cell simple spikes. The x1T/x0BG stimulus consisted of head

movement paired with an earth-stationary target plus a background moving exactly

with the head. This stimulus drove the climbing fibers to respond in a manner similar

to their response during x0T/x0BG training, which induces a decrease in VOR gain

(compare x0T/x0BG and x1T/x0BG in Fig. 3.6, see Appendix for individual neural

responses). In contrast, the simple spike responses of the Purkinje cells were

eliminated in Monkey L, and reversed in Monkey E (Fig. 3.6), so that it was more

similar to the response elicited by the x2T/x2BG stimulus, which induces an increase

in VOR gain. At the behavioral level, training with the x1T/x0BG stimulus induced a

decrease in VOR gain in both monkeys (Fig. 3.6c), as one might predict from the

climbing fiber response.

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3.2.6 Purkinje cell simple spike and climbing fiber signals together predict

behavioral changes

Our results indicate that motor learning can occur when information about the required

direction of learning is carried by both the climbing fibers and Purkinje cell simple

spikes, or when only one of these two instructive signals is available. To evaluate how

instructive signals carried by the simple spikes and climbing fibers may interact during

the induction of motor learning in the VOR, we recorded climbing fiber and Purkinje

cell simple spike responses to many novel training stimuli, and measured the

effectiveness of each training stimulus at inducing VOR learning. Each training

stimulus paired head movements with a different combination of target and

background motion. In total, 16 training stimuli were compared in Monkey L, and 17

training stimuli were compared in Monkey E. These stimuli elicited different

combinations of instructive signals in the climbing fibers and Purkinje cell simple

spikes (Fig. 3.7a), which allowed us to analyze their individual contributions to

learning. Some pairs of training stimuli elicited similar Purkinje cell simple spike

responses, but different climbing fiber responses (e.g. compare stimuli b and e in

Monkey L; Fig. 3.7a). Other pairs of training stimuli elicited similar climbing fiber

responses, but different Purkinje cell simple spike responses (e.g. compare stimuli e

and i in Monkey L; Fig. 3.7a).

67

We conducted a pairwise analysis to evaluate how each neural signal may contribute

to learning. For each pair of training stimuli, we calculated the difference in climbing

fiber response (∆climbing fiber), the difference in Purkinje cell simple spike response

(∆simple spike), and the difference in learning (∆learning). The 16 training stimuli

used in Monkey L yielded 120 pairwise comparisons, and the 17 training stimuli used

in Monkey E yielded 136 comparisons.

If climbing fibers provide instructive signals guiding learning, then difference in

learning induced by any pair of training stimuli should be related to the difference in

the climbing fiber signals that they elicit during training. Indeed, ∆climbing fiber was

linearly correlated with ∆learning (Fig. 3.7b). We then divided all the stimulus pairs

into three groups on the basis of the ∆simple spike value associated with each pair.

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For a given ∆climbing fiber, ∆learning systematically varied with ∆simple spike.

Moreover, ∆learning was linearly correlated with ∆simple spike, particularly when the

data were grouped according to ∆climbing fiber (Fig. 3.7c). This influence of both

∆simple spike and ∆climbing fiber on ∆learning is consistent with an independent

contribution of both the climbing fibers and simple spikes to learning.

The contributions of the two putative instructive signals to learning were estimated by

using the average correlation coefficients obtained from the pairwise analyses to

predict the behavioral changes induced by each training stimulus (see Methods).

When only the signals in the climbing fibers were considered, the predicted learning

was well correlated with the observed learning (R2 = 0.85, Monkey L; 0.78, Monkey

E), however, the amount of learning was generally underestimated by 30-40% (Fig.

3.7d, correlation coefficient = 0.72 in Monkey L, 0.61 in Monkey E). The learning

predicted from the two neural signals together was well correlated with the observed

learning (R2 = 0.89, Monkey L; 0.81, Monkey E). Moreover, the correlation

coefficients were close to the ideal value of 1.0, which would be attained if the

predicted learning was equal to the observed learning (black lines, Fig. 3.7d,

correlation coefficient = 1.02 in Monkey L, 0.86 in Monkey E). Thus, a linear

combination of climbing fiber and Purkinje cell instructive signals better accounted for

motor learning in the VOR, compared to climbing fiber signals alone.

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3.3 Discussion

We designed visual-vestibular stimuli that did not affect baseline firing, but eliminated

instructive signals carried by neurons that have been implicated in VOR learning. The

x0T/x2BG training stimulus eliminated putative instructive signals carried by the

climbing fibers, and the x1T/x0BG training stimulus eliminated putative instructive

signals carried by the Purkinje cell simple spikes, yet each of these training stimuli

induced robust motor learning in the VOR. Thus, our data indicate that neither

instructive signals carried by climbing fibers nor instructive signals carried by

Purkinje cell simple spikes are necessary for learning. Rather, each of these neural

instructive signals may operate in parallel, with each capable of inducing learning in

the other’s absence.

3.3.1 VOR circuit physiology and Marr-Albus-Ito model

Models of motor learning in the VOR are constrained by a great deal of information

about how activity at each site in the circuit should affect the gain of the VOR.

Purkinje cells are the sole output of the cerebellar cortex, therefore plasticity

mechanisms in the cerebellar cortex can influence the gain of the VOR only via their

effects on Purkinje cell output during VOR performance, which is tested during head

turns in total darkness. For a change in Purkinje cell output to cause a decrease in

VOR gain, the Purkinje cells should fire less during contraversive head movement

and/or more during ipsiversive head movement, so that inhibitory input from the

Purkinje cells to VOR interneurons in the vestibular nucleus counteracts some of the

excitatory drive to the interneurons from the vestibular afferents, which increase their

firing during ipsiversive head movement. On the other hand, a decrease in inhibition

from the Purkinje cells during ipsiversive head movement would create a bigger

response in the VOR interneurons, and hence, an increase in VOR gain (Fig. 3.1).

The Marr-Albus-Ito model attributed motor learning to climbing fiber-triggered

changes in the weights of parallel fiber synapses onto Purkinje cells. For the

70

appropriate changes in Purkinje cell output to be accomplished by LTD at the parallel

fiber-to-Purkinje cell synapses, there should be selective depression of those parallel

fibers that fire during one specific phase of the head movement. A decrease in VOR

gain would require selective depression of those parallel fibers that provide excitatory

input to the Purkinje cells during contraversive head movement, whereas an increase

in VOR gain would require selective depression of the parallel fibers that fire during

ipsiversive head movement (Fig. 3.1). Since climbing fiber activation can trigger LTD

in parallel fibers active simultaneously[74], the responses present during the

x0T/x0BG and x2T/x2BG training stimuli would be expected to trigger LTD in the

appropriate vestibular parallel fibers to account for the observed changes in VOR gain

(Fig. 3.1). In contrast, there is no reason to believe that the x0T/x2BG training

stimulus could induce selective LTD of the appropriate parallel fibers to produce the

observed decrease in VOR gain. During x0T/x2BG training, the climbing fibers fired

with the same probability during ipsiversive and contraversive head movement,

making it equally probable that parallel fibers active during contraversive or

ipsiversive head movements would undergo LTD. LTD of both groups of parallel

fibers may decrease the average firing rate of the Purkinje cells, but should not cause

the change in firing rate modulation during head movements required to decrease the

VOR gain. Moreover, if climbing fiber activity at the spontaneous rate is not effective

at inducing LTD, then there may be no LTD at the parallel fiber-to-Purkinje cell

synapses during x0T/x2BG training.

3.3.2 Could another population of climbing fibers induce changes in VOR gain?

During standard, x0T/x0BG and x2T/x2BG, training stimuli, the population of

climbing fibers we recorded carried robust signals, which were previously

hypothesized to guide VOR learning. We compared the responses in the same

individual neurons to many different training stimuli. The very same climbing fibers

that carried no instructive signals during our novel training stimuli (x0T/x2BG,

x0.5T/x1BG, and x1.5T/x0.5BG) carried robust and uniform instructive signals during

standard training (x0T/x0BG or x2T/x2BG, Fig. 3.2), and therefore our sample is

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drawn from the same population that has been previously implicated in VOR learning.

It is not likely that there is another population of unrecorded climbing fibers that carry

signals during the training stimuli that elicited no responses in the recorded climbing

fibers. Within the floccular complex, we tested all of the Purkinje cells we isolated

with any task-related

activity, and found no

significant response of their

climbing fiber inputs during

the x0T/x2BG training

stimulus (P > 0.05, one-

sample t-test; Fig. 3.8). It is

unlikely that climbing fibers

in other parts of the

cerebellum carry instructive

signals to guide motor

learning in the VOR. There

is compelling evidence from several labs that VOR learning requires the floccular

complex, and not other regions of the cerebellum. Floccular Purkinje cells undergo

learned changes in their output during VOR performance, which should support the

observed changes in VOR gain (Fig. 3.1)[13, 15-17]. Lesions of the floccular

complex completely abolish VOR gain learning[19, 67]. In contrast, lesions of other

regions of the cerebellum that receive vestibular input, including the uvula/nodulus,

lobule VI, and lobule VII[75, 76], do not affect VOR learning[67-69].

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It is also unlikely that a

subpopulation of the climbing

fibers we recorded during the

x0T/x2BG stimulus can

account for the decreases in

VOR gain induced by this

stimulus. Although the

population response during

the x0T/x2BG stimulus was

not significantly different

from zero, there was variation

around the mean response of

zero (Fig. 3.2). Therefore, a

subset of the individual

climbing fibers (21/39) had small responses in the correct, “gain decrease” direction

during the x0T/x2BG stimulus to account for the observed decrease in VOR gain.

However, if one considers the cycle-by-cycle variability of the responses, only 2/21

climbing fibers had significant “gain decrease” responses during the x0T/x2BG

stimulus. Moreover, 18 climbing fibers had small responses in the incorrect, “gain

increase” direction, three of which were significant. Thus, any effects of the climbing

fibers with small “gain decrease” responses should be cancelled by the effects of the

equal number of climbing fibers with small “gain increase” responses, unless the

Purkinje cells receiving the “gain decrease” climbing fibers have unique properties

that endow them with privileged control over the behavior, and there was no evidence

for that. The neurons receiving small “gain increase” versus “gain decrease”

instructive signals during x0T/x2BG training were indistinguishable in all other

respects (Fig. 3.9, Table 3.2). Finally, the responses of the full population of climbing

fibers during x0T/x2BG training were normally distributed, consistent with the

responses being drawn from a single, uniform population of neurons rather than two

distinct populations (P = 0.26; D’Agostino-Pearson test). Thus, the variation around

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the mean climbing fiber response of zero during the x0T/x2BG stimulus appears to be

biological noise, rather than any kind of signal that could guide the induction of the

observed behavioral changes.

3.3.3 Other instructive signals for cerebellum-dependent motor learning

In the absence of climbing fiber instructive signals, learning was correlated with the

Purkinje cell simple spike responses present during training, suggesting that the

signals carried by the simple spikes may contribute to the induction of learning.

Purkinje cell simple spikes have been previously proposed as an instructive signal for

motor learning[1]. Purkinje cells are well positioned to control the induction of

plasticity in their main target, the vestibular nucleus (VN) or deep cerebellar nuclei

(DCN), and there are several lines of evidence for changes within the VN/DCN during

learning[24, 45, 77]. The cellular mechanisms for such changes are not fully

understood, but it is plausible that the correlation of activity between the vestibular

afferent and Purkinje cell inputs to the vestibular nucleus could induce synaptic

changes that decrease or increase VOR gain.

Although some models of cerebellum-dependent learning include sites of plasticity

outside the cerebellar cortex, plasticity at these sites has been viewed as secondary to

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LTD at the parallel fiber-to-Purkinje cell synapses (pf-Pk LTD), or dependent on pf-

Pk LTD for their appropriate expression. For example, a model of eyeblink

conditioning proposes that pf-Pk LTD causes changes in the Purkinje cell simple spike

output, which in turn induces plasticity in the DCN[54]. According to this model,

learned changes in the DCN are not expressed if the pf-Pk LTD is reversed[78]. Other

recent work has suggested that the consolidation of cerebellum-dependent learning

involves a transition from an initial dependence on pf-Pk LTD to a dependence on

other plasticity mechanisms [50, 77]. Thus, despite the accumulating evidence that

multiple plasticity mechanisms contribute to learning, climbing fiber-triggered

plasticity has been viewed as central to and necessary for cerebellum-dependent

learning. In contrast, our current results suggest that instructive signals carried by

Purkinje cell simple spikes may induce learning in the absence of any climbing fiber-

triggered plasticity.

Our experiments measured the neural

instructive signals available to guide

learning at the beginning of training,

when the gain of the VOR was at

baseline. Each training stimulus was

presented for just 1-2 min so that the

responses of a single neuron to many

stimuli could be compared. It is

possible that climbing fiber responses

to the training stimuli could emerge as

learning progresses, even if no

response was present at the beginning

of training. However, most of the

behavioral changes occurred early in

the training session (Fig. 3.10) and

therefore cannot depend on any late-

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developing climbing fiber responses. Thus, even if climbing fibers did make a small

contribution to learning late in x0T/x2BG training, it would have to be secondary to a

climbing fiber-independent plasticity mechanism that drove the initial changes in the

circuit.

3.3.4 Multiple instructive signals during cerebellum-dependent motor learning

Our results suggest that neither instructive signals carried by the climbing fibers nor

the Purkinje cell simple spikes are necessary for motor learning. However, either

neural instructive signal appears to be sufficient to support learning, thus imparting the

cerebellum with distinct, independent ways to control the induction of motor learning.

We cannot rule out the possibility that other neural instructive signals, besides those

carried by climbing fibers and Purkinje cell simple spikes, could contribute to the

induction of learning. However, for the range of stimuli tested, the learned changes in

behavior were well predicted from the signals present in the climbing fibers and

simple spikes during training. The pairwise analysis in Fig. 3.7 suggests that changes

in VOR gain are a linear function of the climbing fiber response amplitude during

training and a linear function of the Purkinje cell simple spike response amplitude

during training. When both neural instructive signals are present during training, their

effects appear to sum linearly, suggesting that climbing fiber and Purkinje cell

instructive signals operate independently and in parallel during the induction of

learning.

The independent operation of multiple instructive signals offers three advantages.

First, it provides more than one way to achieve a similar behavioral outcome. The

learning induced by x0T/x2BG training and x1T/x0BG training is similar at the

behavioral level, in each case a decrease in VOR gain. However, different neural

instructive signals are available during each training stimulus. Therefore, the

underlying memory traces may be quite different, potentially involving distinct

locations within the circuit. Second, the use of multiple instructive signals could allow

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learning to occur under a broader range of conditions, since each neural instructive

signal encodes different aspects of the learning environment, with the climbing fibers

more sensitive to motion of the visual background and the Purkinje cells more

sensitive to the visually-driven eye movements made during training. Third, when

multiple instructive signals are recruited, more learning is induced. Thus, to optimize

motor learning in clinical or other settings, one should design the training environment

to recruit each of the available instructive signals.

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3. 4 Materials and methods

Surgical preparation, calibration, behavioral experimentation, and data analysis were

performed as described in Chapter 2.

To test for temporally-restricted climbing fiber responses, each cycle was divided into

ten 200-ms epochs and the average probability of a complex spike was calculated for

each epoch. When spontaneous activity was used as a control condition, climbing

fiber responses were divided into 2000-ms “trials” and aligned to the onset of

recording, and the same analysis was performed as on the training stimuli.

The baseline probability of climbing fiber firing was estimated from all recordings of

spontaneous activity in climbing fibers. Spike trains of spontaneous activity were

divided into 200-ms bins, and the probability of complex spike firing was calculated

from a random selection, without replacement, of 35 200-ms bins (the typical number

of bins used to calculate the firing probability during a training stimulus). This

measure was repeated to derive the mean and standard deviation used as the baseline

probability.

Pairwise comparison analysis

Pairwise comparison analysis in Fig. 3.7 was based on the neural and behavioral

responses to 16 training stimuli for Monkey L and 17 training stimuli for Monkey E.

For each training stimulus, the average response in the climbing fibers and Purkinje

cell simple spike populations were calculated by averaging the components of the

individual neural response aligned with peak head velocity. For learning, the median

value of all behavioral replications was used. For each pair of training stimuli, the

differences in Purkinje cell simple spike responses, climbing fiber responses, and

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learning were calculated. For Monkey L, the 16 training stimuli yielded 120 total

comparisons, and the 17 training stimuli yielded 136 total comparisons for Monkey E.

To assess the contribution of climbing fiber signals to learning, the range of

differences in Purkinje cell simple spike responses was divided into five bins of equal

size, and each stimulus pair was sorted into a bin according to its difference in

Purkinje cell simple spike response. A linear regression was performed between the

differences in learning and the differences in climbing fiber signals for the stimulus

pairs in a given bin. The correlation coefficients obtained from the five bins of

stimulus pairs were averaged, and this value (CCF) was used to estimate the climbing

fiber contribution to learning.

To assess the contribution of simple spike signals to learning, the range of differences

in climbing fiber responses was divided into five bins of equal size, and each stimulus

pair was sorted into a bin according to its difference in climbing fiber response. A

linear regression was performed between the differences in learning and the

differences in simple spike signals for the stimulus pairs in a given bin. The

correlation coefficients obtained from the five bins of stimulus pairs were averaged,

and this value (CSS) was used to estimate the Purkinje cell simple spike contribution to

learning.

The coefficients, CCF and CSS, derived from the pairwise analysis were used to predict

the amount of learning induced by each training stimulus. To predict the amount of

learning based only on the climbing fiber instructive signals present during each

training stimulus, the following equation was used:

LCF(stimulus) = CCF * CFmeasured(stimulus)

where LCF(stimulus) is the predicted learning for a training stimulus and

CFmeasured(stimulus) is the measured climbing fiber response during that stimulus.

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To predict the amount of learning, Ltotal(stimulus), based on both Purkinje cell simple

spike and climbing fiber responses, the amount of learning predicted from each signal

was calculated and then summed:

Ltotal(stimulus) = CCF * CFmeasured(stimulus) + Css * SSmeasured(stimulus)

where SSmeasured(stimulus) is the measured simple spike response during the training

stimulus.

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Chapter 4 Specificity and generalization of motor learning in the VOR

4. 1 Introduction

Once motor learning has occurred, it can only be useful if it is expressed in

appropriate contexts. To some extent, learning must be able to generalize to contexts

slightly different from those used for training, otherwise natural situational variations

may restrict learning from ever being expressed. On the other hand, learning can be

maladaptive if expressed in an inappropriate context.

One example of context dependency of motor learning in the VOR is the specificity of

adapted gain to head tilt present during training. In a previous study, gain up training

of rotational VOR with the head tilted at 90 degrees to the right induced a gain

increase that was not at all expressed when the head was tilted 90 degrees to the left

[79]. This finding suggests an interaction between neural signals encoding

information from the semicircular canals, which measures head rotation, and the

otolith organs, which measure head tilt. The interactions of these signals during motor

learning in the VOR with head tilt to the left and to the right must activate non-

overlapping populations of neuronal synapses undergoing plasticity since no learning

was expressed when the head was tilted in the direction opposite that present during

training.

Another example of stimulus specificity for motor learning in the VOR is the

expression of learning when measured at head-rotation frequencies different from the

training frequency. If training is induced with visual-vestibular stimuli at a single

frequency, the observed change in VOR gain is much smaller when measured at head

movement frequencies different from the one used for training [22, 80]. This finding

suggests that the circuit for the VOR contains parallel, frequency-tuned signal-

processing channels that can each be modifiable during training. Interestingly, the

amount of training observed at the non-training frequencies is dependent on the

training frequency. For example, if training is done with 0.5 Hz stimuli, there is little

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expression of learning when the VOR gain is measured with 5 Hz head rotation[80].

When training is done with 5 Hz stimuli, however, learning is expressed when VOR

gain is measured at 0.5 Hz [22, 80]. One possible explanation for this observed

difference in generalization with high versus low frequency training is their

dependence on different plasticity mechanisms. Previous studies (and studies from

Chapter 2 and 3) have shown that instructive signals carried by both Purkinje cell

simple spikes and climbing fiber signals could guide learning at low-frequency;

whereas climbing fibers are the best candidate instructive signals to guide plasticity

during high-frequency training [6].

As described in previous chapters, the behavioral manipulations using a target and

visual background paired with head movements to induce motor learning in the VOR

allowed us to titrate the amount of neural instructive signals present in Purkinje cells

and climbing fibers. Examining the generalization profile, or the amount of learning

at the non-training frequencies during training with complex visual-vestibular stimuli,

may provide insights to how climbing fiber-triggered plasticity mechanisms versus

Purkinje cell-triggered plasticity mechanisms contribute to the generalization of motor

learning in the VOR.

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4.2 Results

4.2.1 Generalization of learned changes in VOR gain

Motor learning in the VOR was typically assessed by measuring changes in VOR gain

at the frequency used for training. We also measured the effectiveness of the training

stimulus to induce gain changes at the non-training frequencies, to assess if learning

induced at one frequency generalized to other frequencies. In our experiments, we

induced motor learning in the VOR with visual-vestibular stimuli at 0.5 Hz frequency,

and tested the generalization of learning at 4 other non-training frequencies (0.2, 1, 2

and 5 Hz) in Monkeys L and C. Figure 4.1 plots the learned gain changes at the

training (0.5 Hz) and non-training frequencies (0.2, 1, 2, and 5 Hz), elicited by x0 and

x2 consistent and conflicting training conditions.

In general, the learned changes in VOR gain generalized to non-training frequencies.

Conflicting conditions induced smaller gain changes than consistent conditions at the

training frequency, however, the generalization profiles between consistent and

conflicting conditions were similar, in that the learning induced at the training

frequency generalized to other frequencies.

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To compare the overall pattern of generalization, a generalization index was derived

from the data[8]. It was calculated as the fraction of learning at the training frequency

that was expressed, on average, at test frequencies other than the training frequency. It

was calculated as:

∆Gain(i) represented the percent change in VOR gain measured at one of the j head

rotation frequencies other than the training frequency, and ∆Gain (train) was the

percentage change in gain at the training frequency. Increasing values of the

generalization index indicated increasing extents of generalization. If the average

change in the non-training frequencies was equal to ∆Gain(train), then the index was

1. If the average change in the non-training frequencies was 0, then the index was 0,

indicating specificity. The index is greater than 1 if the changes in the non-training

frequencies were larger than the change at the training frequency. Lastly, the index

was less than 0 if the changes in the non-training frequencies were maladaptive.

The calculated generalization indices for Monkey L and Monkey C during x0T/x0BG

were 0.67 and 0.96, respectively. For

x2T/x2BG in Monkey L and C, the

values were 0.39 and -0.05,

respectively. These results

demonstrated that increases in gain

generalized less than decreases in VOR

gain, and these findings were also

replicated in mice studies[8]. The

smaller changes in VOR gain induced

by conflicting stimuli at training and

non-training frequencies produced a noisy range of generalization indices, which

showed no systematic trend of the effect of conflicting stimuli on generalization (Fig.

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4.2). During these conditions, small absolute changes in gain have a big impact on the

index value.

4.2.2 Can VOR gain changes at the non-training frequencies be explained by a

combination of climbing fiber and Purkinje cell signals?

We analyzed if gain changes at the non-training frequencies can be explained by a

combination of climbing fiber and Purkinje cell signals that were recorded at the

training frequency. If both signals contribute to VOR gain changes at the non-training

frequencies, then we may expect both signals to account for learning, similar to how

both signals account for learning at the training frequency (see Chapter 3). On the

other hand, only one of the two signals may selectively contribute to learning

expressed at the non-training frequencies.

VOR gain changes at the non-training frequencies were measured for the 16

conditions in Monkey L and C. We conducted a pairwise comparison analysis as

explained in Chapter 3 to analyze the contributions of each instructive signal to

learning at non-training frequencies. For each pair of stimuli, we calculated the

difference in Purkinje cell responses, and ranked ordered the pairs according to their

difference. We took pairs of stimuli that were within 10 percent (± ~4 spikes/s) of the

maximal difference in Purkinje cell response (± ~40 spikes/s). These stimuli pairs

represent pairs of training conditions that elicited similar Purkinje cell signals. Next,

we calculated the difference in climbing fiber responses and learning between the

pairs, and plotted these differences. If climbing fiber signals contribute to VOR gain

changes, we would expect a correlation between the difference in learning and the

difference in climbing fiber signals when Purkinje cell responses were held nearly

constant. We observed a correlation in climbing fibers signals elicited by the training

stimulus at 0.5 Hz to VOR gain changes induced at non-training frequencies (0.2, 1, 2,

and 5 Hz) for Monkey L (Fig. 4.3). In Monkey C, there was no correlation at the non-

training frequencies. In Monkey C, the gain changes induced at the training

frequencies were smaller (Fig. 4.1), compared to the changes induced in Monkey L.

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Since the change at the training frequency was small, the VOR gain changes at the

non-training frequencies were also comparably smaller, or no changes were induced

despite robust climbing fiber signals being elicited at the training frequency. These

small changes or lack of changes at the non-training frequencies could account for the

lack of correlation observed in Monkey C.

For each test frequency, we determined the learning rate attributed to climbing fiber

instructive signals by calculating the regression coefficients for the data in Fig. 4.3 for

Monkey L. The values were 16.0% ∆VOR gain/sp/s for 0.2 Hz, 18.8% ∆VOR

gain/sp/s for 1Hz, 16.8% ∆VOR gain/sp/s for 2 Hz, and 11.9% ∆VOR gain/sp/s for 5

Hz. Using these coefficients, we calculated the amount of learning attributed to the

climbing fiber signals by multiplying the learning rate with the measured climbing

fiber response for each training condition. We subtracted the amount of learning

attributed to the climbing fibers from the observed learning. This unaccounted,

residual amount of learning was then compared to the Purkinje cell responses elicited

by the training condition (Fig. 4.4).

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The Purkinje cell responses were correlated to the residual learning during 0.2 and 1

Hz stimuli, moderately correlated during 2 Hz stimuli, and not correlated during 5 Hz

stimuli (Fig. 4.4). These results suggest that Purkinje cell signals can account for

learning-related changes not attributed to climbing fiber signals during 0.2, 1, and 2

Hz test frequencies. Regression coefficients were calculated for each frequency in

Figure 4.4 to determine the learning rate associated with Purkinje cell signals. The

Purkinje cell and climbing fiber learning rates were then used to predict the individual

contributions of each signal to learning. With the exception of learning-related

changes at 5 Hz, a linear combination of Purkinje cell and climbing fiber signals can

account for learning-related changes at non-training frequencies (Fig. 4.5).

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Our visual-vestibular stimuli elicited many different combinations of Purkinje cell and

climbing fiber responses, and some visual-vestibular stimuli elicited no response in the

climbing fiber response (e.g. x0T/x2BG), while other stimuli elicited no response in

the Purkinje cells (e.g. x1T/x0BG).

According to the linear model that predicts learning using both signals, Purkinje cells

would account for nearly 100 percent of the learning when climbing fiber signals were

absent, and account for very little learning when the Purkinje cell responses were

absent. We asked if the contributions of Purkinje cell and climbing fiber signals to

VOR gain change were similar at training versus non-training frequencies. We took

our 16 conditions in Monkey L and at

each test frequency (with the exception

of 5 Hz where there was no correlation

between learning and Purkinje cell

signals), calculated the VOR gain change

predicted by the Purkinje cell signals,

and divided it by the total gain change

predicted by the Purkinje cell and

climbing fiber signals. These

calculations reflect the percentage of the

total learning attributed to the Purkinje

cell instructive signal. We then compared the percentage of the total learning

attributed to the Purkinje cell signals at the training frequency to the percentages at the

non-training frequencies (Fig 4.6). At the non-training frequencies, Purkinje cells

consistently accounted for 5-15 percent more of the total learning. Both Purkinje cells

and climbing fiber signals were needed to account for the total change in VOR gain at

non-training frequencies. However, a greater Purkinje cell contribution at non-training

frequencies suggest that Purkinje-cell triggered plasticity mechanisms contributed

relatively more to the generalization of learned changes in VOR gain.

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4.3 Discussion

4.3.1 Neuronal population coding schemes to achieve generalization and

specificity

Generalization and specificity of motor learning in the VOR can each be achieved by a

population coding scheme that features either sparse encoding or overlapping

representations. In sparse encoding, different contexts are represented in non-

overlapping population of neurons. Therefore, during a specific training context a

distinct set of neurons are activated and modified. For learning to be expressed, the

modified pathway is only triggered if the specific context is present, since non-training

condition cannot activate the modified pathway. On the other hand, in a network

using overlapping representations, different contexts are represented by overlapping

population of neurons. In this model, a specific training context will activate and

modify a distinct set of neurons, but since these neurons can also be activated by other

contexts, learning can be expressed in a non-training condition.

Neurons in the circuit for the VOR have been found to have different tuning

properties. In the vestibular nucleus, neurons exhibit a wide range of tuning to head-

rotation frequency, with some neurons encoding a specific frequency, while others

encode a wide range of frequencies [81, 82]. The tuning properties of granule cells in

the cerebellar cortex have not been studies, but the large number of granule cells may

permit each neuron to be tightly tuned to a particular context when a movement is

made [2, 4]. Therefore, one possible hypothesis has been that learning expressed in a

narrow range of stimuli would be mediated by plasticity in the cerebellar cortex,

whereas learning that generalized broadly would be mediated by plasticity in the deep

cerebellar nuclei (DCN) or vestibular nucleus (VN).

4.3.2 Generalization of VOR gain increases versus decreases

Since increases in VOR gain generalize less than decreases in VOR gain, this “cortex-

nucleus” model suggests that increases in VOR gain are more dependent on plasticity

in the cerebellar cortex, whereas decreases in VOR gain are dependent on plasticity in

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the vestibular nucleus. This hypothesis, however, is at odds with several studies of

cerebellum-dependent learning. Post-training lesions of the cerebellar cortex resulted

in similar effects on both learned increases and decreases in VOR gain [83, 84]. In

another form of cerebellum-dependent motor learning, eyeblink conditioning,

disconnecting the cerebellar cortical input to the DCN did not degrade the specificity

of learned eye blinks for the tone used in conditioning, suggesting that plasticity in the

DCN supported stimulus-specific learning. Additionally, our current study

demonstrated that VOR gain changes at non-training frequencies can be best

accounted for by both Purkinje cell simple spike and climbing fiber instructive signals.

Since the simple spikes are more likely to guide plasticity in the vestibular nucleus,

whereas climbing fibers are more likely to act on neurons in the cerebellar cortex, our

results in combination with previous studies suggest that it is unlikely that increases

and decreases in VOR gain rely on entirely separate sites in the VOR circuit.

4.3.3 Patterns of generalization for high versus low frequency training

Previous studies have shown that when VOR learning is induced with low frequency

stimuli (0.5 Hz), learned changes in VOR gain do not generalize as well as when VOR

learning is induced with high frequency stimuli (5 Hz) [22]. The “nucleus-cortex”

model would predict that changes induced by low frequency stimuli would be

localized to the cerebellar cortex, whereas changes induced by high frequency stimuli

would be localized to the brainstem.

However, studies of the available neural instructive signals to guide VOR learning

predict opposite outcomes. During high frequency training (5 Hz), Purkinje cell

simple spikes do not provide instructive signals that can discriminate between training

conditions that induce a gain increase versus gain decrease, and therefore, it is unlikely

any changes in the brainstem could account for the observed generalization of

learning. Rather, the best candidate instructive signal is the climbing fiber [6].

However, climbing fibers do project to the vestibular nucleus [33] and could mediate

plasticity events that can be expressed in non-training contexts.

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To successfully stabilize images on the retina across varying conditions, the circuit for

the VOR must coordinate the specificity or generalization of learning across many

dimensions of sensory input, including both vestibular stimulus parameters and

context cues. For example, the vertical VOR can be adapted to different gains during

upward, versus downward, head turns[85]. Subjects can be trained to have different

horizontal VOR gains when their eyes are angled upward versus downward[86].

Learning is also specific for the vergence angle held by the eyes during training[87].

Motor learning in the translational VOR is expressed only in one eye, if the other is

not required to move in response to the training stimulus[88]. In the real world,

learning is regulated by all of these dimensions simultaneously, and so the underlying

coding and plasticity mechanisms in the VOR must be of sufficient richness to enable

regulation of learning by multiple dimensions of sensory input.

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4.4 Materials and Methods

Surgical preparation, calibration, behavioral experimentation, and data analysis were

performed as described in Chapter 3, except that changes in VOR gain were assayed at

five frequencies (0.2, 0.5, 1, 2, and 5 Hz). All frequencies tested were used in the

calculation of the generalization index.

Pairwise comparison analysis in Fig. 4.3 was based on the neural and behavioral

responses to 16 training stimuli for Monkey L and Monkey C.

For each training stimulus, the average response in the climbing fibers and Purkinje

cell simple spike populations were calculated by averaging the components of the

individual neural response aligned with peak head velocity. For learning, the median

value of all behavioral replications was used. The difference in learning was

calculated for all test frequencies.

For each pair of training stimuli, the differences in Purkinje cell simple spike

responses and learning were calculated. To assess the contribution of climbing fiber

signals to learning, the range of differences in Purkinje cell simple spike responses

among the stimulus pairs was determined, and pairs within 10 percent (approx. 4

sps/s) of the maximal difference were extracted. For each test frequency, a linear

regression was performed between the differences in learning and the differences in

climbing fiber signals for the extracted stimulus pairs. In total, five correlation

coefficients (CCF) were calculated, each coefficient specific to a test frequency (0.2, 1,

2, 5 Hz).

To assess the contribution of simple spike signals to learning, the amount of learning

attributed to climbing fiber signals was first calculated for each test frequency by the

following equation:

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LCF(stimulus, frequency) = CCF(frequency) * CFmeasured(stimulus)

where LCF(stimulus, frequency) is the predicted learning for a training stimulus at a

specific test frequency and CFmeasured(stimulus) is the measured climbing fiber

response elicited by a training stimulus. Next, the amount of learning unaccounted by

climbing fiber signals (Lnon-CF) was calculated:

Lnon-CF(stimulus, frequency) = Lmeasured(stimulus, frequency) - LCF(stimulus,

frequency)

where Lmeasured(stimulus, frequency) is the amount of measured VOR gain change

elicited by a training condition at a specific test frequency. For each test frequency, a

linear regression was performed between Lnon-CF(stimulus, frequency) and the

Purkinje cell simple spike signals elicited by each training condition. In total, five

correlation coefficients (Css) were calculated, each coefficient specific to a test

frequency (0.2, 1, 2, 5 Hz).

For each test frequency, the coefficients, CCF and CSS, were used to predict the amount

of learning induced by each training stimulus. To predict the amount of learning,

Ltotal(stimulus, frequency), based on both Purkinje cell simple spike and climbing

fiber responses, the amount of learning predicted from each signal was calculated and

then summed:

Ltotal(stimulus, frequency) = CCF(frequency) * CFmeasured(stimulus) +

Css(frequency) * SSmeasured(stimulus)

where SSmeasured(stimulus) is the measured simple spike response during the training

stimulus.

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Chapter 5 Conclusions and future directions

The dominant theory for the last several decades has postulated that the climbing

fibers provide the neural instructive signals that control the induction of cerebellum-

dependent learning. However, studies in this thesis showed that climbing fiber-

independent plasticity can also support cerebellum-dependent learning, because motor

learning in the vestibulo-ocular reflex (VOR) can be induced with training stimuli that

do not elicit responses in the climbing fibers. We also generated evidence that the

simple spike output of cerebellar Purkinje cells provides the neural instructive signals

guiding the climbing fiber-independent component of motor learning; in the absence

of instructive signals in the climbing fibers, learned changes in VOR gain were

correlated with the simple spike responses during training.

The results from our current study suggest that the simple spike output of Purkinje

cells plays a role in providing the instructive signals guiding motor learning, but a

more limited role than the climbing fibers (Fig. 3.7). This may be because the set of

training stimuli used in the current study elicited only modest simple spike responses

(up to about ± 20 sp/s), compared to the physiological range (at least ± 50 sp/s),

whereas the climbing fiber responses elicited by the training stimuli (up to about ± 1

sp/s) represents a greater portion of their physiological range. Therefore it is possible

that the climbing fiber-triggered component of learning was near maximal whereas the

simple spike-triggered component was not maximal, but could actually make a

substantially larger contribution than these initial results revealed.

A future direction is to design visual-vestibular training stimuli to elicit different

simple spike responses, spanning much of the physiological range. Two general

approaches may be used—first, “stronger” training stimuli such as x3, x4, etc.

conditions should elicit bigger eye movements and therefore larger simple spike

responses during training. Climbing fiber instructive signals tended to saturate during

x0 and x2 consistent training conditions, so climbing fiber signals should remain

94

unaffected by these stronger stimuli. Second, it is possible to stabilize the target on

the retina during head movements to gain control over target and background slip, and

thereby eliminating climbing fiber responses[89]. Image stabilization during target

motion that elicits large eye movements could create big responses in the simple

spikes during head movements. The results of these experiments could reveal answers

to several questions: How much can the simple-spike triggered plasticity contribute to

changes in VOR gain? Does simple spike-triggered plasticity contribute equally to

increases and decreases in VOR gain? Does simple spike triggered plasticity

contribute to learned changes in VOR phase? To what extent do simple spike-

triggered changes in VOR generalize? Can climbing fiber activity regulate the simple

spike-triggered plasticity mechanism?

These future experiments would provide correlative evidence to support the role of

simple spike activity in the induction of motor learning in the VOR. However, such

correlations could be observed if the instructive signals come not from the Purkinje

cells, but another population of neurons whose activity covaries with the simple spike

activity of Purkinje cells. To test the sufficiency of Purkinje cell output for inducing

learning, one future direction is to test whether direct activation of the Purkinje cells

can induce learning when paired with head movements in the absence of visual

stimuli. Direct electrical stimulation of floccular Purkinje cells can be conducted in

primates or mice, while optogenetic activation using the channelrhodopsin-2 system

can be accomplished in mice [90].

What is the algorithm a neural circuit uses to learn? More specifically, for

cerebellum-dependent learning, which neurons are monitoring the accuracy of

movements, and how do they determine when the circuit controlling a movement

needs to be modified? The question of what controls the induction of plasticity in vivo

is one of the most central questions one can ask about the neural mechanisms of

learning, but it has not been studied extensively. For many decades, studies of

cerebellum-dependent learning have focused on the role of the climbing fibers in

95

carrying the neural instructive signals that control the induction of

plasticity. However, the studies in this thesis have shown that climbing fiber-

independent mechanisms also contribute to cerebellum-dependent learning. With the

insight that cerebellum-dependent learning is not a unitary process, the next step is

undertaking systematic research to dissect cerebellum-dependent learning into its

component parts, and identify the molecular, cellular, and electrophsyiological

underpinnings of each.

96

Appendix

Individual climbing fiber and Purkinje cell simple spike responses for each

training condition

The following are polar plots of individual climbing fiber and Purkinje cell simple

spike responses for each of the training conditions. Each point on the polar plots

represents the response recorded in a single Horizontal Gaze Velocity Purkinje cell

(HGVP) in Monkey L (purple), Monkey C (green) and Monkey E (orange). Distance

from the origin represents the amplitude of the response (change in firing rate above

and below baseline firing rate) and the phase represents the timing of the neural

response relative to peak head velocity. Clockwise rotation represents increased phase

lead.

97

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100

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