05-09-BrainBuilding-Abaccus

8/7/2019 05-09-BrainBuilding-Abaccus

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LargeLarge--scale projects to buildscale projects to build

artificial brainsartificial brains:: ABACCUS.ABACCUS.

Wodzisaw Duch (Google: Duch)Wodzisaw Duch (Google: Duch)

Department of Informatics,Department of Informatics,

Nicolaus Copernicus University,Nicolaus Copernicus University,Torun, PolandTorun, Poland

School of Computer Engineering,School of Computer Engineering,

NanyangNanyang TechnologicalTechnological University (NTU),University (NTU),

SingaporeSingapore

Building Artificial Brain workshop after ICANN 2005, Sept 15, 2005


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AttentionAttention--Based Artificial Cognitive ControlBased Artificial Cognitive Control

Understanding System (ABACCUS)Understanding System (ABACCUS)

Large EU integrated project (>150 pp), with 9 participants:Large EU integrated project (>150 pp), with 9 participants:

Kings College London (John G. Taylor, coordinator), UKKings College London (John G. Taylor, coordinator), UK

Centre for Brain & Cognitive Development, Berkbeck College,Centre for Brain & Cognitive Development, Berkbeck College,University of London, UKUniversity of London, UK

Cognition and Brain Sciences Unit, Medical Research Council, UKCognition and Brain Sciences Unit, Medical Research Council, UK

Robotics and Embedded Systems, Technical University of Munich, GRobotics and Embedded Systems, Technical University of Munich, G

Institute of Neurophysiology and Pathophysiology,Institute of Neurophysiology and Pathophysiology,Universittsklinikum HamburgUniversittsklinikum Hamburg--Eppendorf, GEppendorf, G

Institute of Computer Science, Foundation for Research andInstitute of Computer Science, Foundation for Research andTechnologyTechnology Hellas, Heraklion, Crete, GRHellas, Heraklion, Crete, GR

National Center for Scientific Research Demokritos, Athens, GRNational Center for Scientific Research Demokritos, Athens, GR

Dipartimento di Informatica, Sistemistica, Telematica, Universita diDipartimento di Informatica, Sistemistica, Telematica, Universita diGenova, IGenova, I

Dep. of Informatics, Nicholaus Copernicus University, Torun, PLDep. of Informatics, Nicholaus Copernicus University, Torun, PL

Now changed to Brain as Complex System (BRACS)Now changed to Brain as Complex System (BRACS)


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ABACCUS Goals

Assumption: gross neuroanatomical brain structure is critical for itsAssumption: gross neuroanatomical brain structure is critical for itsfunction, therefore it should be preserved.function, therefore it should be preserved.

To demonstrate how fusion of the appropriate brainTo demonstrate how fusion of the appropriate brain--based models,based models,

guided by the overall architecture of the brain, and by itsguided by the overall architecture of the brain, and by its

developmental learning stages, can help attain highdevelopmental learning stages, can help attain high--level cognitivelevel cognitive

processing capabilities.processing capabilities. Show basic language understanding and reasoning abilities for directShow basic language understanding and reasoning abilities for direct

humanhuman--machine communication, at the level of a premachine communication, at the level of a pre--school child,school child,

mimicking solutions used by the human brain.mimicking solutions used by the human brain.

Develop an attention control systems for focusing in sensoryDevelop an attention control systems for focusing in sensory

surveillance tasks, and for image searchingsurveillance tasks, and for image searching.. Development of control structures for autonomous machinesDevelopment of control structures for autonomous machines..

Create its own goals in an autonomous fashionCreate its own goals in an autonomous fashion..

Founded on neuroFounded on neuro--scientific understanding of attention and thescientific understanding of attention and the

sensory and motor systems it controls, development in childrensensory and motor systems it controls, development in children,,

simplified modeling, computer power.simplified modeling, computer power.


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Sketch of the ABACCUS system

Rough sketch of the ABACCUS system, based on simplifiedRough sketch of the ABACCUS system, based on simplified

spiking neurons.spiking neurons.

Computational Platform, Simulation Environment and Integration

Neuroscience and Development

Vision

Memory

System

Drive andIntrinsic reward

system

Atomizationsystem

ReasoningSystem

Feedback AttentionControl system

Motor Control

Speech Tactile

Learning ofPFC goals

WorkingMemory

Value Maps Action/Objectreward system


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Primary objective 1

To develop linguistic powers of ABACCUS system.To develop linguistic powers of ABACCUS system.

Use training of single words by associating their representations toUse training of single words by associating their representations to

internal representations of objects and actions.internal representations of objects and actions.

Use pairUse pair--wise associations to learn word pairs (like kick ball),wise associations to learn word pairs (like kick ball),

extend syntactically and functionally the use of function words.extend syntactically and functionally the use of function words.

Working memory modules, with associated phonological coding,Working memory modules, with associated phonological coding,

will be created and fused in the language component, both forwill be created and fused in the language component, both forspeech understanding and generation.speech understanding and generation.

Extension for abstract concepts by tagging the associated words toExtension for abstract concepts by tagging the associated words to

clusters of concrete object/action representations.clusters of concrete object/action representations.


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Primary objective 2

To create a highTo create a high--level cognitive system, able to solve problemslevel cognitive system, able to solve problems

requiring reasoning, thinking, imagination and creativity.requiring reasoning, thinking, imagination and creativity.

Based on the basic control concept of a forward model, acting asBased on the basic control concept of a forward model, acting as

a predictor and working under attention control.a predictor and working under attention control.

Forward models include various semantic and goal networks.Forward models include various semantic and goal networks.

Sequences of activations of representations will be learnt andSequences of activations of representations will be learnt and

thereby used to achieve goals.thereby used to achieve goals.

Various of these forward models will be present and branch intoVarious of these forward models will be present and branch intoeach other during the running process by means of lateraleach other during the running process by means of lateral

connections.connections.

New routes will occur allowing complex goals to be achieved, orNew routes will occur allowing complex goals to be achieved, or

even new, previously unrecognized goals, to be arrived ateven new, previously unrecognized goals, to be arrived at

(required for creativity).(required for creativity).


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Example of action internal models

Rough sketch of the ACTIONRough sketch of the ACTION

subnetwork.subnetwork.

Sensory information enters theSensory information enters the

supplementary motor area (SMA)supplementary motor area (SMA)

(1) cortico(1) cortico--thalamothalamo--cortical (thecortical (the

short loop);short loop);

(2) cortico(2) cortico--striatalstriatal--GPiGPi--thalamothalamo--

cortical (the long loop);cortical (the long loop);

(3) cortico(3) cortico--(STN(STNGPe)GPe)--GPiGPi--thalamothalamo--cortical (first indirect loop);cortical (first indirect loop);

(4) cortico(4) cortico--striatalstriatal--(STN(STNGPe)GPe)--GPiGPi--

thalamothalamo--cortical (the secondcortical (the second

indirect loop).indirect loop).


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Primary objective 3

Extract a simplified architecture for the attention control system.Extract a simplified architecture for the attention control system.

Should involve both sensory and motor control, especially jointShould involve both sensory and motor control, especially joint

sensorysensory--motor control, systems whose creation is guided bymotor control, systems whose creation is guided by

neuroscientific knowledge about the brain.neuroscientific knowledge about the brain.

Approach based on infant development to train ABACCUSApproach based on infant development to train ABACCUS

incrementally on the computer platform.incrementally on the computer platform.

Use neuroscientific data to guide the architecture of a largeUse neuroscientific data to guide the architecture of a large--scalescale

neural simulation of the relevant components of the human brain.neural simulation of the relevant components of the human brain.

Use feedUse feed--forward/feedback training simulations based on theforward/feedback training simulations based on the

simplified brain architecture following developmental processes,simplified brain architecture following developmental processes,

using the sensor and response signals of the robotic embodiment.using the sensor and response signals of the robotic embodiment.


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Primary objective 4

Developing the ability to learn novel objects, both as stimuli andDeveloping the ability to learn novel objects, both as stimuli and

as associated reward values.as associated reward values.

Use reward/penalty feedback training with attention control, withUse reward/penalty feedback training with attention control, with

associated value maps constructed to learn to encode the valuesassociated value maps constructed to learn to encode the values

of stimuli and responses to them in the environment.of stimuli and responses to them in the environment.

Temporal sequence or schemata (as object/action sequences asTemporal sequence or schemata (as object/action sequences as

well as the associated rewards involved) will be constructed towell as the associated rewards involved) will be constructed to

function as predictors and so support reasoning, along withfunction as predictors and so support reasoning, along with

automatic response learning.automatic response learning.

Neural codes of visual, auditory and tactile concepts will be learntNeural codes of visual, auditory and tactile concepts will be learnt

in a feedforward manner, without attention, as will codes forin a feedforward manner, without attention, as will codes for

motor responses controlling the embodied robot.motor responses controlling the embodied robot.


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Primary objective 5

Develop a robotic embodiment; this involves:Develop a robotic embodiment; this involves:

Specification of robots available for ABACCUS control.Specification of robots available for ABACCUS control.

Development of sensor and response systems in the robot.Development of sensor and response systems in the robot.

Sensory data mappings to the central computational platform.Sensory data mappings to the central computational platform.

An environmental task domain in which ABACCUS can develop itsAn environmental task domain in which ABACCUS can develop its

concepts and cognitive powers.concepts and cognitive powers.

Simulation platform for the developmental training (on a BeowulfSimulation platform for the developmental training (on a Beowulf

cluster or similar powerful computer system).cluster or similar powerful computer system).

Specifications for integration of various models on the centralSpecifications for integration of various models on the central

computational platform.computational platform.

Compatibility of computational languages used by partners.Compatibility of computational languages used by partners.


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Attention Control

Attention control systems will be achieved by use of the early flow ofAttention control systems will be achieved by use of the early flow ofactivity from lowactivity from low--level cortex rapidly to prefrontal sites.level cortex rapidly to prefrontal sites.

This initially uncoded activity will be used to bias feedback activityThis initially uncoded activity will be used to bias feedback activity

sent to parietal and/or temporal lobe, so as to bias the attentionsent to parietal and/or temporal lobe, so as to bias the attention

feedback from there down to the activated inputs encoded in thefeedback from there down to the activated inputs encoded in theobject representations in the temporal lobe.object representations in the temporal lobe.

This feedback loop will be trained by causal Hebbian learning, withThis feedback loop will be trained by causal Hebbian learning, with

the attention feedback represented by contrast gain amplification, sothe attention feedback represented by contrast gain amplification, so

by sigmaby sigma--pi network quadratic nodes, onto neurons in hierarchicallypi network quadratic nodes, onto neurons in hierarchically

lower modules; the goal representations will be learnt as part of thislower modules; the goal representations will be learnt as part of this

processing.processing.

The manner in which a dualThe manner in which a dual--control stereo camera can be usedcontrol stereo camera can be used

most effectively will be explored by modeling the manner in whichmost effectively will be explored by modeling the manner in which

eye and attention control are separable.eye and attention control are separable.


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Drives, Value Maps & Emotions

Drives and reflexes will be enumerated and modeled as separateDrives and reflexes will be enumerated and modeled as separatesystems to be used as a launching pad for more controlledsystems to be used as a launching pad for more controlled

developmental sensory representations and responses.developmental sensory representations and responses.

The rewards system will also be developed as a separateThe rewards system will also be developed as a separate

component, based on the learning of reward/error prediction.component, based on the learning of reward/error prediction.

ABACCUS will be trained by reward error to develop value maps forABACCUS will be trained by reward error to develop value maps for

objects and responses as goals, to bias learning of feedbackobjects and responses as goals, to bias learning of feedback

attention to filter out responses in complex environments. The effectattention to filter out responses in complex environments. The effect

of the reward signal will be checked to determine if there is anyof the reward signal will be checked to determine if there is any

modification of the earlier goal representations learnt undermodification of the earlier goal representations learnt under

attention control.attention control.

Value maps associated to rewards attached to sequences of stimuliValue maps associated to rewards attached to sequences of stimuli

(schemata) will give emotional bias in the cognitive decision(schemata) will give emotional bias in the cognitive decision--makingmaking

it can perform.it can perform.


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Short-term working memory

Two sorts of working memory are presently known: buffer orTwo sorts of working memory are presently known: buffer or

slave working memories, of a fixed and limited persistence, andslave working memories, of a fixed and limited persistence, and

longer lasting prefrontal activities of duration determined by thelonger lasting prefrontal activities of duration determined by the

importance and difficulty of the goal they represent.importance and difficulty of the goal they represent.

Buffer memory is fed by the semantic representations, whoseBuffer memory is fed by the semantic representations, whoseactivity they extend, and thereby allow the development ofactivity they extend, and thereby allow the development of

attended state estimators in the various modalities for moreattended state estimators in the various modalities for more

general use across other cortical regions, especially goal sites.general use across other cortical regions, especially goal sites.

Buffer sites will be modeled by suitable recurrent circuits coupledBuffer sites will be modeled by suitable recurrent circuits coupledto the semantic maps from which they receive input.to the semantic maps from which they receive input.

Rehearsal of the working memory will be extended to variousRehearsal of the working memory will be extended to various

transformations that are also known to occur in goal sites, suchtransformations that are also known to occur in goal sites, such

as deletion, comparison and related functions.as deletion, comparison and related functions.


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Concept Understanding and Abstraction

HighHighlevel concepts will be created by use of a hierarchy oflevel concepts will be created by use of a hierarchy of

networks, each being more specific in its features to which it isnetworks, each being more specific in its features to which it is

responsive than the next lower one.responsive than the next lower one.

The understanding based on such ontology allows to understandThe understanding based on such ontology allows to understand

various levels of concepts, by the associated supervarious levels of concepts, by the associated super-- or subor sub--ordinate concept levels activated by trained connectivity at theordinate concept levels activated by trained connectivity at the

levels of specificity allowed by the number of hierarchical layers.levels of specificity allowed by the number of hierarchical layers.

Abstraction will arise through creation of specific connections of aAbstraction will arise through creation of specific connections of a

new word representation, describing the concept, to its variousnew word representation, describing the concept, to its various

more concrete word and object/action representatives.more concrete word and object/action representatives.

Word representations will be linked to emotion state descriptors,Word representations will be linked to emotion state descriptors,

that are connected to the associated reward value mapthat are connected to the associated reward value map

representations of the associated system state. For example therepresentations of the associated system state. For example the

word anger would be associated with the underlying state arisingword anger would be associated with the underlying state arising

from blockage, by the environment, of the gaining of a goal.from blockage, by the environment, of the gaining of a goal.


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Basic tasks

2D domain, enclosed within four walls with obstacles on the floor2D domain, enclosed within four walls with obstacles on the floor

and 2D objects on the walls, of varying shapes (square, triangle,and 2D objects on the walls, of varying shapes (square, triangle,

circle) and colors (red, green, blue), emitting various tones.circle) and colors (red, green, blue), emitting various tones.

Objects are potential goals, to be learnt and responded to.Objects are potential goals, to be learnt and responded to.

Perceptions/Concepts: To develop internal representations of thePerceptions/Concepts: To develop internal representations of the

2D objects, based on shape/color feature maps created by2D objects, based on shape/color feature maps created by

learning, after relevant clustering on the feature map activations,learning, after relevant clustering on the feature map activations,

to learn a representation for the objects.to learn a representation for the objects.

Actions/Goals: learn to touch/manipulate the 2D objects based onActions/Goals: learn to touch/manipulate the 2D objects based on

stimulusstimulus--action rewards and fused representation as a goal; toaction rewards and fused representation as a goal; to

attentionally bias lower order perceptual representations justattentionally bias lower order perceptual representations just

learnt (using dopaminelearnt (using dopamine--based reward learning).based reward learning).


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Intermediate tasks

2D domain with walls, with 3D objects as potential goals; objects2D domain with walls, with 3D objects as potential goals; objects

with simple shapes (sphere, box, ring), colors, emitting variouswith simple shapes (sphere, box, ring), colors, emitting varioussounds, some in motion (to help develop tracking).sounds, some in motion (to help develop tracking).

Perception/Concepts/Goals: To develop internal representations ofPerception/Concepts/Goals: To develop internal representations of

the 3D objects, based on a set of their 2D views, clustered overthe 3D objects, based on a set of their 2D views, clustered over

time (by use of binocular representations of depth). Certaintime (by use of binocular representations of depth). Certainexamples of moving stimuli will be created so as to be learnt.examples of moving stimuli will be created so as to be learnt.

Actions/rewards: Motor responses to move specific objects toActions/rewards: Motor responses to move specific objects to

certain places, such as to stack an object on top of another, orcertain places, such as to stack an object on top of another, or

place one object inside another (object fusion observed developingplace one object inside another (object fusion observed developingin both chimps and human infants).in both chimps and human infants).

Language: Word maps for actions and objects concepts will beLanguage: Word maps for actions and objects concepts will be

created, using associations of the word maps to the relevant objectcreated, using associations of the word maps to the relevant object

maps; attention will be important in this learning process.maps; attention will be important in this learning process.


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Advanced task domain

Perception/Concepts/Goals: complex 3D objects present withPerception/Concepts/Goals: complex 3D objects present withvarious features in various modalities, including animal shapesvarious features in various modalities, including animal shapes

emitting sounds and smells. The internal concept representationsemitting sounds and smells. The internal concept representations

will be extended to hierarchies of concepts, so leading towill be extended to hierarchies of concepts, so leading to

abstraction processes (where more abstract, basic level conceptsabstraction processes (where more abstract, basic level concepts

are activated, such as that for animal by specific animal inputs, soare activated, such as that for animal by specific animal inputs, soleading to spread of activity to related concepts and to associatedleading to spread of activity to related concepts and to associated

reasoning potentialities).reasoning potentialities).

Actions: with added complex tasks requiring reasoning to achieveActions: with added complex tasks requiring reasoning to achieve

fast/accurate responses: manipulating objects, putting objects intofast/accurate responses: manipulating objects, putting objects into

slots/matching shapes, Tower of Hanoi (involving virtualslots/matching shapes, Tower of Hanoi (involving virtual

movements of objectsmovements of objects translations and stackingtranslations and stacking at a fixed set ofat a fixed set of

different sites), Incident Detection, etc. AIdifferent sites), Incident Detection, etc. AI--based approaches tobased approaches to

such tasks exist (cf. SOAR), but ABACCUS will be based on thesuch tasks exist (cf. SOAR), but ABACCUS will be based on the

flexible posterior representation transformation, goal creating andflexible posterior representation transformation, goal creating and

resolving powers of a neural system.resolving powers of a neural system.


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More on advanced task domain

Language powers will be expanded so that ABACCUS can be talked to,Language powers will be expanded so that ABACCUS can be talked to,using limited length word strings (initially of length two or three). Theseusing limited length word strings (initially of length two or three). These

strings will be understood in terms of the associated object/action mapsstrings will be understood in terms of the associated object/action maps

and responses made by the system, either as actions or actionand responses made by the system, either as actions or action

sequences or suitably meaningful speech responses. Reasoning andsequences or suitably meaningful speech responses. Reasoning and

creation of new types of responses will be done by use of lateralcreation of new types of responses will be done by use of lateral

spreading in memory to achieve new trajectories in the schemata space.spreading in memory to achieve new trajectories in the schemata space.

The Incident Detection & Tracking Scenario involves a robot moving inThe Incident Detection & Tracking Scenario involves a robot moving in

the internal environment, although with novel stimuli and obstacle layout.the internal environment, although with novel stimuli and obstacle layout.

This includes ramps and stationary or slow moving obstacles. CandidateThis includes ramps and stationary or slow moving obstacles. Candidate

events of interest in this paradigm are:events of interest in this paradigm are:

a)a) Moving entity detection and classification (humans, animals).Moving entity detection and classification (humans, animals).

b)b) Static environment variation detection (unexpected objects).Static environment variation detection (unexpected objects).

c)c) Abnormal auditory events.Abnormal auditory events.

d)d) Abnormal visual events.Abnormal visual events.


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Summary

ABACCUS is the most ambitious project formulated so far,ABACCUS is the most ambitious project formulated so far,an attempt to simulate functions of most brain structures.an attempt to simulate functions of most brain structures.

High order functions (including consciousHigh order functions (including conscious--like behavior?)like behavior?)

should result from elementary interactions in a system withshould result from elementary interactions in a system withproper brainproper brain--like architecture.like architecture.

Neuroscience and developmental science integrated in oneNeuroscience and developmental science integrated in one

computational model, useful for behavioral experiments andcomputational model, useful for behavioral experiments andcomputational psychiatry.computational psychiatry.

Wish us good luck ...Wish us good luck ...

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