1 IJCNN 2010 Panel Between Bottom-up and Top-Down What Is the Much in-between? Wednesday July 21,...

1

IJCNN 2010 Panel

Between Bottom-up and Top-DownWhat Is “the Much in-between”?

Wednesday July 21, 4:50pm, Room 123Presentation Panel Members:

Paolo ArenaEdgar Koerner

Asim Roy Ron Sun Yan Meng

Giogio MettaAngelo CangelosiJanusz A. Starzyk John G. Taylor

Narayan Srinivasa

Juyang Weng

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Questions for the Penal1. Does the brain use symbolic representation in a fashion similar

to our symbolic AI? Why? What lesson can we learn?

2. Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed reasoning? Why?

3. How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? Between bottom and top, what is the “much in-between”?

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Questions for the Penal1. Does the brain use symbolic representation in a fashion similar

to our symbolic AI? Why? What lesson can we learn?

2. Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed reasoning? Why?

3. How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? Between bottom and top, what is the “much in-between”?

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Embodied cognitive capabilities from simple brains

Paolo Arena

University of Catania

ITALY

[email protected]

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(1) L. Chittka and J. Niven, “Are bigger brains better?” Current Biology 19, 2009 (2) Shettleworth, S.J. 2001: Animal cognition and animal behaviour, – Animal Behaviour 61: 277-286.(3) van Swinderen, B. and Greenspan, R.J. (2003) Salience modulates 20–30 Hz brain activity in Drosophila. Nat. Neurosci. 6, 579–586(4) V. Srinivasan, Evidence for counting in insects, Anim Cogn (2008)(5) R. Strauss et al. “Analysis of a spatial orientation memory in Drosophila”, Nature 453, 1244-1247, 2008 (6) V. Srinivasan et al. “Grouping of visual objects by honeybees” J. Exp. Biol., 2004(7) Jan Wessnitzer*, Place memory in crickets, Proc. R. Soc. B (2008)

Simple animals (insects) are not only reflex automataThey show (individually) capabilities like numerosity (4) , attention and categorisation-like processes (6) , sameness vs difference (1), water maze solution (7)

considered as clear traits of cognition (2)

in a limited neuron number (105-106) and simpler organization than mammals. Drosophila Melanogaster is a cognitive poor cousin of the Bee, but genetic tools are available to unravel the structure-function-behavior loop (5)

Some Capabilities of a simple Brain

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•Orientation•object detection• classification• memory

•Olfactory learning •Context generalization from visual info•Behav. Evaluation

Toward an insect brain model

EnvironmentEnvironment

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Heisenberg, Nature, 2003

Some key aspects of simple Brains

TI

ME

Rabinovich et al., 2003J. Comp. Neurosci.

Axo-axonal horizontal connections among KC in locust MBs for space-time processing

Advanced polymodalCalices (Bee). Strausfeld et al. J. Comp. Neurology (2009)

Connectionist models of simple brains can show goal-directed reasoning as:Simple forms of sequence learning based on cascades of environmentally induced correlations

…following - P. Arena et al. IEEE TRANS. on N.NET.(2009)

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From sensory data distribution to symbol-like representation:

What is “in-between” ?

Edgar Koerner

Honda Research Institute Europe

Offenbach, Germany

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From signal to symbol processing

prediction modulates processing based on acquired knowledge about scene and context

short- and long-term memory of scenes, actions and its outcomes

predicted distancewarning

…

multi cue fusion using learning

scene rep.: integration / prediction

context / situation / task

image

common preprocessing

cue #1 cue #2 cue #n

• learned cue relevance from environment (day, night, rain)

• use of domain knowledge e.g. predicted road area

• predicted car positions knowledge of expected featurescontext / task

learn

)( 224 RfR

Ra

multilateral topologicalinteraction

weight

unique neuronal & topological task dependent fusion strategy

learn

cues

for simple reactive systems, direct linking works sufficiently good

How to link sensory data distribution to symbolic knowledge representation ?

intermediate representations seem to be required for robust and flexible problem solving in a complex environment

How to define intermediate representations - by sensory data structure?

Complex interaction in real world environment requires anchoring of knowledge representation to sensory data distributions

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What determines intermediate representations in the brain?

The cortex stores in its hierarchical structure a hierarchy of representations, like features, objects, scenes, concepts, …

The basic wiring of the representational hierarchy is genetically encoded, referenced to the subcortical behaviour control.

This is a-priori knowledge, acquired while interacting with the environment during phylogenetic development.

Learning only fills in the blanks, adding specific experience based on sensory data.

Fellemann & van Essen 1991

What is stored at which location in the cortex - is not defined by sensory data structure, - but by pointers from its phylogenetically older subcortical reference structure (behaviour control).

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From activity distributions to symbols cortical area m cortical area k

activity at the upper cortical layers is abundant (activity distribution), lower layers 5 & 6 show sparse activation patterns (symbol quality)

The large pyramidal neurons of layer 5 (Py5) of the columnar structures are the read-out neurons to behaviour control.

Without subcortical structures no behaviour!

The cortex memorizes experience of successful behaviour for reuse under similar situations.

Behaviour related subcortical inputs control processing and learning at the respective columns.

The Py5 are fishing for useful information to control behaviour. Subcortical inputs can modulate all pyramidal neurons in a column.

processing of afferent data distributions

processing of afferent data distributions

afferentinput

◦ ◦◦lateralassociation

afferentinput

lateralassociation

1

2

3

4

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behaviour control output

cortex

thalamusbehaviour control output

subcortical structures(behaviour control)

subcortical structures(behaviour control)

top-downtop-down

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Repetitive instantiation of “symbols” in the brain?

There is no single “symbolic” knowledge representation level on top- Each behavioural output may instantiate a decision (symbolic quality) based on data distributions shaped by its afferent, efferent, and association inputs- Complexity of symbol content increases with hierarchic representation level

Seemingly, there is no „intermediate“ representation without a behavioural relevance

Behaviour includes both interaction with environment (overt) internal simulation for planning and decision making (covert)

hierarchic levels

symbol

activitydistribution

symbol

symbol

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Behaviour control needs define the “in-between”

For defining the representational architecture of autonomously interacting systems we have to elucidate the relational architecture of the targeted behaviour, but not to start with data structure

The system does not describe the outside world from an observer’s point of view, but it describes its interaction with the environment from a subjective point of view – using the metric of its control space for defining the “in-between” levels of representation

◦◦◦

◦ ◦ ◦

afferent data distribution

symbolic knowledgerepresentation

afferent data distribution

behaviour output

◦ ◦ ◦◦◦◦

◦◦ ◦

◦◦◦

Intermediate representations in the brain are not essentially defined by sensory data structure, but by the relational architecture of behaviour control.

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Your TitleAsim Roy

Arizona State University

Tempe, Arizona 85287, USA

[email protected]

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Q1: «Does the brain use symbolic representation in a fashion similar to our symbolic AI? Why? What lesson can we learn?»

Is localist representation = symbolic representation?

Jeff Bowers has published a paper or two arguing for the viability of grandmother cells -- cells that represent whole "objects" such as a specific face (or your grandmother's face)

Connectionists claim they are at the symbolic level too and can do symbolic computation (Smolensky and others)

Connectionists don’t deny the symbolic level

The argument is not about symbolic level, but about the form and nature of representation and computation

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ReferencesBowers JS (2009). On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience. Psychological review, 116 (1), 220-51 PMID: 19159155

Bowers JS (2010). More on grandmother cells and the biological implausibility of PDP models of cognition: a reply to Plaut and McClelland (2010) and Quian Quiroga and Kreiman (2010). Psychological review, 117 (1) PMID: 20063980

Plaut, D., & McClelland, J. (2010). Locating object knowledge in the brain: Comment on Bowers’s (2009) attempt to revive the grandmother cell hypothesis. Psychological Review, 117 (1), 284-288 DOI: 10.1037/a0017101

Smolensky, P. (1988). On the proper treatment of connectionism. The Behavioral and Brain Sciences, 11, 1-74.

Smolensky, P. (1995). Connectionism, constituency and the language of thought. In Macdonald, C., & Macdonald, G. (Eds.), Connectionism. Cambridge, MA: Blackwell.

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Q2: «Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks

cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed sensor-based reasoning? Why? What lesson can we learn?»

There is ongoing work on neural implementation of symbolic logic

Van der Velde and de Kamps have proposed a neural blackboard architecture for sentence structure analysis

It allows for combinatorial (or compositional) structures

Frank van der Velde and Marc de Kamps (2006). Neural blackboard architectures of combinatorial structures in cognition. Behavioral and Brain Sciences, 29 : 37-70

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Q3: «Symbolic AI architectures start from abstract concepts and connectionist architectures start from concrete receptors (e.g., pixels). Hybrid architectures use both.

What is the "much in-between" --- between concrete sensory inputs and abstract concepts?»

Almost all useful knowledge is built bottom-up; that’s why you have years and years of schooling and practice

You may be taught all the grammar rules of a language, but you won’t be able to write good essays unless you practice for years and years

You can read all the books and watch all the video about playing a game (soccer, tennis or swimming), but you can’t play the game unless you practice

OLD AI - rules are given to you and all you have to do is store and use them But it doesn’t work – try learning math without ever solving a problem

In many cases, rules are given to you to guide learning, but they are not part of the operational system

Abstract concepts need to be translated into operational networks Roy, A. 2000. On Connectionism, Rule Extraction and Brain-like Learning. IEEE Transactions on Fuzzy Systems, 8, 2, 222-227.

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Q4: «How do you think about the brain/artificial architecture for autonomous development? As the brain is "skull-closed," how does it fully autonomously

develop its internal representations for the "much in-between" through experience, from one task to the next?»

NSF Report (2007) – “This strong dependence on human supervision is greatly retarding the development and ubiquitous deployment autonomous artificial learning systems.”

We need to develop autonomous algorithms – ones that don’t depend on human babysitting for fine tuning of parameters

It’s time to either fix our algorithms or invent new ones

We are interested in forming working groups to create new kinds of algorithms

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Autonomous Learning and Development within CLARION

How a hybrid cognitive architecture (CLARION, as a psychological theory) interacts with the world and learns

Ron Sun

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CLARION Theory: Basic Ideas(psychologically realistic, comprehensive theory

of the mind) Hybrid connectionist-symbolic system Dual representation – dual-process theory of the mind

(two levels or more --- implicit, explicit, meta-cognitive, etc.; Evans and Frankish, 2009)

Capable of both explicit goal-directed reasoning (e.g. in action decision making), as well as implicit reactive responses (from pattern recognition)

The two levels interact with each other Bottom-up learning and top-down learning: key to

autonomous learning and development Motivational and meta-cognitive processes: secondary

control of behavior

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CLARION: Answers to the Questions Does the brain use symbolic representation in a fashion similar to our symbolic AI? Why?

What lesson can we learn? Yes, to a certain extent. But that’s a tiny part. Majority is implicit, embodied, gradual, and/or

reactive, as illustrated by CLARION and associated psychological evidence (see Sun, 2002; Sun et al., 2005, Psyc Rev; Helie and Sun, 2010, Psyc Rev).

Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed reasoning? Why?

Yes. Connectionist models can perform reasoning, implicitly or explicitly. See Sun (1994, 2002, 2011). But what is the point?

Both bottom-up and top-down learning are needed for human learning. Their interactions are crucially important (for developing reasoning and beyond).

How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? What is the “much in-between”?

Humans learn through interacting with the world. In the process, rely heavily on implicit learning, bottom-up learning (as well as top-down learning), and on meta-cognitive control and regulation, on the basis of the motivational underpinnings (as in CLARION). Also learn through interacting with the social and cultural environment (imitation, communication, teaching, etc.).

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A Few Pointers to the Literature on the CLARION Theory, Data, and

Implementation R. Sun, Anatomy of the Mind. Oxford University Press, New York. 2011. R. Sun, Duality of the Mind. Lawrence Erlbaum Associates, Mahwah, NJ. 2002.

S. Helie and R. Sun, Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, 2010.

R. Sun, Motivational representations within a computational cognitive architecture . Cognitive Computation, Vol.1, No.1, pp.91-103. 2009.

R. Sun, P. Slusarz, and C. Terry, The interaction of the explicit and the implicit in skill learning: A dual-process approach . Psychological Review, Vol.112, No.1, pp.159-192. 2005.

R. Sun, E. Merrill, and T. Peterson, From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, Vol.25, No.2, pp.203-244. 2001.

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Between Bottom-Up and Top-Down What Is «the Much in-between»?

Yan Meng

Department of Electrical and Computer Engineering

Stevens Institute of Technology

Hoboken, NJ, USA

Email: [email protected]

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Q1: Does the brain use symbolic representation in a fashion similar to our symbolic AI? Why? What lesson can we learn?

All representation are symbolic. A symbol represents something else by association, resemblance, or convention. The real questions is what level of abstraction is necessary to reproduce the functionality of a brain, assuming a perfect model? How can we enable an AI system to modify/extend its abstraction.

Q2: Connectionist models have shown progress in pattern recognition, but they have been largely used as a classifier or regressor. Some said that artificial neural networks cannot perform reasoning. Is there "a light in the tunnel" for connectionist models to perform goal-directed sensor-based reasoning? Why? What lesson can we learn?

It isn’t the issue of capability, but rather efficiency. One can devise a case study that is narrow or simple enough to solve with a feasibly-sized ANN, that doesn’t solve the underlying scalability problem nor bring us any closer to a system with general reasoning capabilities. ANN is useful as an interface between sensor inputs and more abstract reasoning modules in hybrid AI systems.

Q3: How do you think about the brain/artificial architecture for autonomous development? As the brain is "skull-closed," how does it fully autonomously develop its internal representations from one task to the next? What is the “much-in-between”?

Brain does not start as a random network that self-organizes based on experience, brain starts with some basic set of primitives and basic assumptions which must be highly self-extensible. New knowledge gained from experience/stimuli and existing knowledge used to interpret experiences.

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What is “Much in between”? and Knowledge Organization• There is a hierarchy of abstraction

starting from the least abstract (“raw sensory patterns) and progressing through multiple levels of increasing abstraction

• Each level is basically indentifying patterns in the level below itself

• Higher abstract concepts are built from relationships among and generalizations of lower abstract concepts

Input Sequence

BCM

GRN

CMA-ES

EnvolvingTuning

E-BCM-GRN Model

Input

Output

textLabel 1 Label 2 …….

(Y. Meng, Y. Jin, J. Yin, and M. Conforth, IJCNN 2010)

Input Frames…...

Spatial Features

(0,0) (0,1)

(1,0) (1,1)

(m+1,n+1)

…...

…...

…...

…...

…...

(1,1)

(m,n)

…...

…...…...

…...

…...

Middle Layers

Higher Layer

Neurons

…...

Long Term Memory

Context Hierarchy

Working Memory

UnconsciousProcesses


Short Term Memory

SensoryMemory

ActivePerception

PassivePerception

Short TermEpisodicMemory

PerceptualMemory

ProceduralMemory

SemanticMemory

EpisodicMemory

Short TermPerceptual

Memory

Reflex

Stimuli

Action

Conscious Focus

ConsciousVolition

IntrinsicMotivation

PreservationDrives

UnconsciousSkill


Problem

Solution



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Fusing Bottom-up Learning and Top-down Learning in Object Recognition

• NN Architecture– One input layer, one output layer,

multiple hidden layers– Independent bi-directional connections

between adjacent layers• Data flows

– Bottom-up pathway generates hypotheses

– Top-down pathway produces predictions– Fusion techniques integrate two-pathway

info • Learning

– Gradient descent learning for both batch and online visual object recognition tasks

vy

D

uy

ix

jx

L

uvc

ivw

vjp

(Y. Zheng, Y. Meng, and Y. Jin, IJCNN 2010)

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… in betweenGiorgio Metta

Italian Institute of Technology & University of Genoa

Genoa, ITALY

[email protected]

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Some of the “in between” (in the brain)

Mirror neurons: motor neurons that are activated when “seeing” someone else’s hand performing a manipulative action and in performing the same action

Found also in f4, parietal cortex, but mirror effects more widespread that initially thought

From: Fadiga, L., L. Fogassi, V. Gallese, and G. Rizzolatti, Visuomotor Neurons: ambiguity of the discharge or "motor“ Perception? Internation Journal of Psychophysiology, 2000. 35: p. 165-177.

The type of action seen is relevant

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Action and visual perception

Understanding mirror neurons: a bio-robotic approach. G. Metta, G. Sandini, L. Natale,

L. Craighero, L. Fadiga. Interaction Studies. Volume 7 Issue 2. 2006

Classification(recognition)

Grasping actions

Visual space Motor space

Object affordances (priors)

Exp. I(visual)

Exp. I I(visual)

Exp. I I I(visual)

Exp. I V(motor)

Training

# Sequences 16 24 64 24

# of view points 1 1 4 1

Classif ication rate

100% 100% 97% 98%

# Features 5 5 5 15

# Modes 5-7 5-7 5-7 1-2

Test

# Sequences 8 96 32 96

# of view points 1 4 4 4

Classif ication rate

100% 30% 80% 97%

Individual A Individual BVMMVMM

Vision Classifier

Fv, OkGi

Vision ClassifierVMM

Fv, Ok Fm, OkGi

Learned by backpropagation ANN

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Action and speech perceptionaudio

features extraction

classifier

AMM

speech

b,p,d,t

audiofeatures

motorfeatures

w/ motor audio

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Giorgio’s answers to the questions Does the brain use symbolic representation in a fashion similar to our symbolic AI?

Why? What lesson can we learn?Not necessarily, but the brain can “build” punctuated representations of events and objects via mechanisms similar to affordances (links between generated actions, target objects and perceived effects).


Manipulation of “symbols” (affordances) is reasoning and this manipulation can be implemented by means of artificial neural networks.

How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? What is the “much in-between”?A possible architecture (also similar to Shanahan’s) sees two levels of generalization: first from sensorimotor patters to affordances and then from affordances to planning/reasoning.

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Embodied ConnectionismAngelo Cangelosi

Centre for Robotics and Neural Systems

University of Plymouth, UK

[email protected]

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Embodied Cognition: The Case of Action and Language

There are two opposing theoretical approaches to the study of language and cognition (in humans and cognitive systems/robots)

1. Language is autonomous and amodal (e.g. Fodor, Chomsky, Landauer & Dumais)

2. Language is integrated with cognition and grounded in the world/body (eg. Cangelosi & Harnad, Gallese & Lakoff, Pulvermuller, Glenberg, Barsalou, Ellis et al., Coventry & Garrod ...)

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Embodied Connectionism

“Classical” Connectionism: 1.Fixed semantic feature representation of the environment (input and output units). 2.Network (organism) is passive and is exposed to frequency-based training protocols3.Subsymbolic representations for symbol manipulation tasks

Glenberg, A., (2005). Lessons from the embodiment of language: Why simulating human language comprehension is hard. In Cangelosi A., Bugmann G. & Borisyuk R. (Eds.) (2005). Modeling Language, Cognition and Action: Proceedings of the 9th Neural Computation and Psychology Workshop. World Scientific.

“No fixed set of semantic features can capture the [language embodiment] phenomena; instead the features needed for

understanding (e.g., that a tractor affords raising the body, or that a crutch affords transfer of a soccer ball) arise from an

interaction between types of bodies, human goals, and components of the situation.” (Glenberg, 2005)

Embodied Connectionism (Neuro-robotics): 1.Direct encoding of sensorimotor features (proprioception, vision, motors/actuators…)2.Organism actively explores and interacts with the environment3.Experiments on the development of language and action/language integration

Seidenberg, Plaut et al.

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Experiments on action and language and embodied connectionism

Cangelosi et al (2010). Integration of action and language knowledge: A roadmap for developmental robotics. IEEE Trans. on Autonomous Mental Development..

The Modi experiment: Thinking with your body (Morse et al. 2010).

Stimulus-Response Compatibility Effects and Microaffordance (Ellis et al. 2008; Macura et al. 2009).

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Embodied Connectionism: Answers to the Questions

Does the brain use symbolic representation in a fashion similar to our symbolic AI? Why? What lesson can we learn?

There are no AI-type symbols in the brain, but there are embodied representations and PDP mechanisms that guide symbol-manipulation phenomena. E.g. Words (symbols) are grounded in sensorimotor experience.


Yes. Neuro and developmental robotics allows us to experiment the integration and embodiment of higher-order cognitive skills (e.g. language, action, emotions)

How do you think about the brain/artificial architecture for the autonomous development? As the brain is "skull-closed", how does it fully autonomously develop its internal representation from one task to the next? What is the “much in-between”?

Embodiment, evolution and development can explain the learning of sensorimotor and higher-order cognition capabilities

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Mental Development and RepresentationBuilding through Motivated Learning

Janusz A. Starzyk

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Motor processing

Sensory processing

Rewards and subconscious

processing

Planning and thinking

Motivation and goal selection

Episodic memory

Semanticmemory

Action monitoring

Attention switching

Sensory inputs Motor outputs

Central executive

Top-Down Bottom-Up Interaction

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The goal is to reduce the dominant pain Abstract goals are created to reduce abstract pains Motivation is pain driven

- +

PainDual pain

+

Thirsty

Abstract pain

“glass of water” –

sensory input to abstract pain

center

Sensory pathway(perception, sense)

Motor pathway(action, reaction)

Primitive Level

Level I

Level IIwell

-

w. glass

draw

drink

ActivationStimulationInhibitionReinforcementEchoNeedExpectation

Connectionists Concept Creation

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Motivation Driven Learning

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What is Between Top & Bottomin the Brain?

John G. Taylor

Department of Mathematics,

King’s College London


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In-between Land In the bottom: the ‘dwarves’ working at the coal-face carving

out features & sculpting actions In the top: Consciousness Goals Decisions Emotion values In between: Internal models Attention

SC

Parietal

A

Thal

ACGSF

G

NBM

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Sites of In-Between Land

Inferior Parietal (SMG+AG + …) (?) IPS & Superior Parietal (attn IMC( Superior Temporal (affordances/int models) Supplementary & Premotor Cortices (internal models/mirror

neurons) Function: attn: to awareness Function: int models: to mental simulation/reasoning

(GNOSYS review: JGT et al, Imag & Vision Comp 27:1641-1657, 2009)

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Attention circuits: Ventral & Dorsal (Corbetta et al 2002, 5, 8)

Have 2 BOLD-based circuits:

Dorsal(Endogenous)

Ventral(Exogenous)-As circuit breaker?- By NE?

[Corbetta et al, Neuron 58:306, 2008)]

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Attention Copy Model of Consciousness (CODAM)

IN

ReportSpeed input to reportInhibit distracters

Attention Attention

copy signal(precursor)

IN

OUT

Owner

Amplifyinput

Model of content + owner activity(CODAM, JGT 2000+)

Bias

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Overall Structure

Use functionally-defined modules (IMC, STM, feature analysers, action generators, corollary discharge systems& forward models, goals)

Connectivity genetically by chemical gradients More data to support this from brain imaging (Bressler,

Desimone) Apply architecture to cognitive machines (JGT)

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What is Between Top and Bottom in the Brain ?

Narayan Srinivasa

HRL Laboratories LLC

Malibu, CA USA


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Brain: Computation and Function• Top-down and Bottom-up processing of signals in the brain is a mere abstraction

• Activity at any level can be transmitted to any other level via ascending and descending pathways

• Inhibition plays key roles in termination of a particular computation and enables temporal coordination by balancing excitation

• Signals propagated between brain areas via spikes with oscillations serving as means for packaging spike information

• Neuronal groups that are synchronized in spike activity form dynamic internal representations within brain areas – these groups are degenerate – more than one group can give same output

• Synaptic efficacy modified via spike timing and modulated via rewards or punishments affects the composition of neuronal groups and also the cortical gains during signal transmission

• Re-entrant signaling enabled by large number of reciprocal connections serves to bind activity of neuronal groups in various brain areas in space and time - either forced oscillations, resonant loops or via transient oscillatory coupling

• Four key functions performed by the brain: categorization, analogies, association and prediction

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Key Aspects of Brain Computation

Primary and secondary cortex in each modality

NONSELF World signals

• Laminar Cortical Architecture • Thalamocortical, Corticothalamic, Corticocortical Loops• Degenerate representations

Brainstem,hypothalamus,

autonomic centers

SELF Homeostatic systems & Proprioception

• Neural registration of internal signals (hormones) • Neural registration of muscles & joint states

Internal State Categorization Perceptual Categorization

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Brainstem,hypothalamus,

autonomic centers


Internal State Categorization Perceptual Categorization

Correlation in septum, amygdala

and hippocampus etc

• Signals from self and non-self are associated• Memory of rewards and punishments during these associations are formed

Value-Category Memory


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Perceptual CategorizationCorrelation in

septum, amygdalaand hippocampus etc

Value-CategoryMemory

Memory Formationin Frontal, Temporal and Parietal Areas

• Laminar Cortical Architecture• Corticocortical Loops• Degenerate representations• Associations between value- category memory and perceptual categorizations

Reentrant loop

Conceptual Categorization


53

Embodiment & Emergence of Behaviors

Synaptic plasticity combined within laminar and subcortical structure interactions caused by embodiment in a non-stationary environment leads to emergence of complex behaviors

Brainstem,Hypothalamus,

autonomiccenters


Correlation in septum, amygdala

and hippocampus etc

Memory Formationin Frontal, Temporal and Parietal Areas

SELF NON-SELF

BasalGanglia

Cerebellum

Motor ControlCircuits

Action

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Brain’s Internal Representation: Mixtures of Sensory and Motor Signals

Juyang Weng

Embodied Intelligence LaboratoryDepartment of Computer Science

Cognitive Science Program

Neuroscience Program

Michigan State University

East Lansing MI 48824 USA

http://www.cse.msu.edu/~weng/

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A1 for Q1: There Seems No Symbol in the Brain

• Representation source from environment:

• Receptors• Muscles and glands

• No master mapsDiff. from: Treisman 80; VanEssen 93, Tsotsos 95

• No meaning boundaryDiff. from: Hawkins 09 and many other symbolic methods

Weng IJCNN 2010

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A3 for Q3: Brain’s Representation

QuickTime™ and a decompressor

are needed to see this picture.

Weng & Luciw TAMD 2009

{ (x, z) | x in X, y in Y }Top Z: actions

Bottom X: receptors (e.g., pixels)

In-between: Y as

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A2 for Q2: ED Network for

Intent-Directed Reasoning

Epigenetic Developer (ED) Network:Each area: map (x, y) with (xi, yi) for all i

FA: Finite Automaton andIts probabilistic variants

Proved a relation: From any FA, there is an ED Network

=> ED Networks can reason

Marvin Minsky: ANNs cannot reason well

Weng IJCNN 2010

QuickTime™ and a decompressor

are needed to see this picture.

Luciw &Weng IJCNN 2010

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IJCNN 2010 Panel

Between Bottom-up and Top-DownWhat Is “the Much in-between”?

Wednesday July 21, 4:50pm, Room 123Discussion Panel Members:

Paolo ArenaEdgar Koerner

Asim Roy Ron Sun Yan Meng

Angelo CangelosiJanusz A. Starzyk John G. Taylor

Narayan SrinivasaJuyang Weng

Nik Kasabov

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