V1 Physiology

Questions

Hierarchies of RFs and visual areas

Is prediction equal to understanding?

Is predicting the mean responses enough?

General versus structural models?

What should a theory of V1 look like?

How is information represented in V1?

The cortex

Visual Areas in the Nonhuman Primate

Felleman & van Essen

Visual Areas in the Nonhuman Primate

Monkey LGN

Monkey LGN

Monkey V1 – Laminar organization

Monkey V1 – Inputs

Monkey V1 – Outputs

Monkey V1 – Oculodominance Columns

Monkey V1 – Oculodominance Columns

Monkey V1 – CO patches (or blobs)

Receptive field

Monkey V1 – Orientation Tuning

Monkey V1 – Orientation Columns

Orientation map

What generates the map?

How does it develop? What is the role of experience?

What is its functional significance (if any)?

How are receptive field properties distributed with respect to the map features (such as pinwheels)?

What is the relationship to other maps (retinotopy)?

Monkey V1 – Orientation Map

Orientation columns Monkey V1 – The Ice Cube Model

LGN cell

V1 simple cell

V1 complex cell

Concentric on/off

Simple cells

Complex cells

Hyper-complex

Grandmother

Hierarchy of Receptive Fields

Simple cells receptive fields

Models v0.0

Analysis of monosynaptic connections

Alonso, Usrey & Reid (2001)

Monosynaptic connectivity from thalamus to layer 4

The “sign rule” of thalamo-cortical connectivity

Reid & Alonso (1995) Alonso, Usrey & Reid (2001)

Monosynaptic connectivity from thalamus to layer 4

Expected response of linear RF to moving gratings

Skottun et al (1991)

Yet F1/F0 distributions are bimodal

There appears to be a continuum of responses

Priebe et al, 2004

Priebe et al, 2004

Beware of bounded indices

Laminar distribution of F1/F0

Same in cat (Peterson & Freeman; but see Martinez et al)

Standard Models v1.0

Conditional Stimulus Distributions

How are the original and conditional stimulus distributions different?

p s

P(s) P(s | spike)

Stochastic stimuli

Standard Models v1.1

Elaborating the LN model

Carandini, Heeger & Movshon (1996)

Simple-cell nonlinearities: Saturation

Carandini, Heeger & Movshon (1996)

Saturation depends on orientation

Carandini, Heeger & Movshon (1996)

Simple-cell nonlinearities: Masking

‘Non-specific’ gain control can shape tuning selectivity

Prediction = Understanding?

The linear-nonlinear model

Simple cell receptive fields in V1

Simple cell receptive fields in V1

Simple cell receptive fields in V1

Simple cell receptive fields in V1

Simple cell receptive fields in V1

Why this particular set of filters?

Why is the cortical state important?

Cortical State, ( )s t

Stimulus, ( )x t Response, ( )r t

The response to sensory stimulation at any one time is a function of both the recent history of the stimulus and the cortical state.

If the ongoing cortical activity is noise then:

• Measure the mean response to sensory stimulus

• Measure how the mean response varies with stimulus parameters.

Going beyond the modeling of mean responses

The ‘vending machine’ analogy

Current State, ( )s t

Stimulus, ( )x t Response, ( )r t

Count up to 75¢ and deliver a coke (a deterministic machine)

The vending machine analogy

The ‘vending machine’ analogy

Count up to 75¢ and deliver a coke (a deterministic machine)

0¢

25¢

50¢

The vending machine analogy

The ‘vending machine’ analogy

Current State, ( )s t

Stimulus, ( )x t Response, ( )r t

Count up to 75¢ and deliver a coke (a deterministic machine)

The vending machine analogy

The ‘vending machine’ analogy

Saturday 23, 2004.

The response appears very noisy.On average, we get one positiveresponse every 3 stimuli.

The vending machine analogy

The ‘vending machine’ analogy

Saturday 23, 2004.

The response appears very noisy.On average, we get one positiveresponse every 3 stimuli.

The source of the noise may be inthe mechanism delivering the coke,

The vending machine analogy

Arieli et al (1996)

Single trial response

Single trial prediction

Mean response

Modeling the Mean Response – Is it sufficient?

Modeling the Mean Response – Is it sufficient?

Supèr et al (2003)

Seeking invariants of the population response

8 spikes

17 spikes

3 spikes

Vertical grating

Vertical grating

Vertical grating

Stimulus Response Percept

There must be some invariant feature in the population responses.

Asking about the ‘neural code’ is equivalent to asking what is this invariant (‘best clustering’ approach of Victor et al).

Theory of Visual Area X

Representation: Area X is about representing natural signals optimally.

Estimation/Bayes: Area X is all about estimating the most likely stimulus (motion/contours/etc) given the statistics of natural signals.

Processing: Area X is doing some interesting image processing (for example, face detection)

Behavior: Area X is about using visual information for visually guided behavior (‘active vision’)

Half-Time