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Cognitive Cognitive NeuroscienceNeuroscience

Janusz A. Starzyk

Computational IntelligenceComputational Intelligence

http://grey.colorado.edu/CompCogNeuro/index.php/CECN_CU_Boulder_OReillyhttp://grey.colorado.edu/CompCogNeuro/index.php/Main_Page

Based on a course taught by Prof. Randall O'Reilly University of Colorado,Prof. Włodzisław DuchUniwersytet Mikołaja Kopernikaand http://wikipedia.org/

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The Brain ...The Brain ... The most interesting and the most complex

object in the known universe

How can we understand the workings of the brain?

On what level should we attack this question? An external description won’t help much.

How can we understand the workings of a TV or computer?

Experiments won’t suffice, we must have a diagram and an understanding of operational principles.

To make certain that we understand how it works, we must make a model.

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How do we know anythingHow do we know anything??How do we know anythingHow do we know anything??An important question: how do we know things?

Example: super diet based on dr. K, Chinese medicineand other miracle methods. How do we know thatthey work? How do we know that they are for real?

Gall noticed that the skull shape decides about ones abilities. Thousands of cases confirmed his observations.

Craniometry: measuring the bones of the skull to determine intelligence.

Do I know or I only believe I know?

Not being certain allows to learn, certainty makes learning difficult. If we know how easy it is to err we could avoid a scientific fallacy.

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How to understand the brain?How to understand the brain?How to understand the brain?How to understand the brain?To understand: reduce to simpler mechanisms?

Which mechanisms? Analogies with computers? RAM, CPU? Logic? Those are poor analogies.

Psychology: first you must describe behavior, it looks for explanations most often on a descriptive level, but how to understand them?

Physical reductionism: mechanisms of the brain. Reconstructionism: using mechanisms to reconstruct the brain’s functions

We can answer many questions only from an ecological and evolutionary perspective: why is the world the way it is? Because that’s how it made itself

... Why does the cortex have a laminar and columnar structure?

To create: what must we know in order to create an artificial brain?

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From molecules through neural networksFrom molecules through neural networksFrom molecules through neural networksFrom molecules through neural networks

10-10 m, molecular level: ion channels, synapses, properties of cell membranes, biophysics, neurochemistry, psychopharmacology;

10-6 m, single neurons: neurochemistry, biophysics, LTP, neurophysiology, neuron models, specific activity detectors, emerging.

10-3 m, functional neural groups: cortical columns (104-105), group synchronization, population encoding, microcircuits, Local Field Potentials, large-scale neurodynamics, sequential memory, neuroanatomy and neurophysiology.

10-4 m, small networks: synchronization of neuron activity, recurrence, neurodynamics, multistable systems, pattern generators, memory, chaotic behaviors, neural encoding; neurophysiology ...

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… … to behaviorto behavior … … to behaviorto behavior10-2 m, mesoscope networks: sensory-motor maps, self-organization,

field theory, associative memory, theory of continuous areas, EEG, MEG, PET/fMRI imaging methods ...

10-1 m, transcortical fields, functional brain areas: simplified cortical models, subcortical structures, sensory-motor functions, functional integration, higher psychic functions, working memory, consciousness; (neuro)psychology, computer psychiatry ...

Cognitive effects

Principles of interactions

Neurobiological mechanisms

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Levels of descriptionLevels of description Levels of descriptionLevels of descriptionSummary (Churchland, Sejnowski 1988)

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Systemic levelSystemic level

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… … to the mindto the mind … … to the mindto the mind

Now a miracle happens ...

1 m, CNS, the whole brain and organism:

An interior world arises, intentional behaviors, goal-oriented actions, thought, language, everything that behavioral psychology examines.

Approximations of neural models:

Finite State Machine, rules of behavior, models based on the knowledge of cognitive mechanisms in artificial intelligence.

What happened to the psyche, the internal perspective?

Lost in translation: networks => finished machines => behavior

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A neurocognitive approachA neurocognitive approach

Computational cognitive neuroscience: detailed models of cognitive functions and neurons. Neurocognitive computing: simplified models of higher cognitive functions, thinking, problem solving, attention, language, cognitive and behavioral controls.

Lots of speculation, but qualitative models explaining the results of psychophysical experiments as well as the causes of mental illnesses are developing quickly.

Even simple brain-like information processing yields results similar to the real ones! Forewarning against excessive optimism based on behavioral models.

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INPUT OUTPUT

Simulation or

Real-World System

TaskEnvironment

Agent Architecture

Long-term Memory

Short-term Memory

Reason

ActPerceive

RETRIEVAL LEARNING

From Randolph M. Jones, P : www.soartech.com

Model of transformationModel of transformation

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Model of self-organizationModel of self-organization

Topographical representations in numerous areas of the brain:sensory impulses, in the motor cortex and cerebellum, multimodal maps of orientation inferior colliculus, visual system maps and maps of the auditory cortex.Model (Kohonen 1981): competition between groups of neurons and local cooperation.

Neurons react to signals adjusting their parameters so that similar impulses awaken neighboring neurons.

o

o

oox

x

xx=data

neural network

N-dimensional

xo=weights of neurons

o

o o

o

o

o

o

o

input space

Weights locatepoints in N-D

w 2-D

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Dynamic modelDynamic modelStrong feedback, neurodynamics.

Hopfield model: associative memory, learning based on Hebb’s law, synchronized dynamics, two-state neurons.

Vector of input potentials V(0)=Vini , i.e. input = output.

Dynamics (iterations) Hopfield’s network reaches stationary states, or the answers of the network (vectors of elemental activation) to the posed question Vini (autoassociation).

If the connections are symmetrical then such a network trends to a stationary state (local attractor).t = discrete time. 1 sgn 1 sgni i ij j j

j

V t I t W V

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Biophysical modelBiophysical model – spiking neurons – spiking neurons

EPSP, IPSP)(tI syn

Spike

Spike

SomaSynapses

synCmC mR

synR

“Spiking Neuron Models”, W. Gerstner and W. Kistler Cambridge University Press, 2002

http://icwww.epfl.ch/~gerstner//SPNM/SPNM.html

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Molecular foundationsMolecular foundations

Action potential

Ca2+

Na+

K+

-70mV

Ions/protein

Action potentials are the result of currents which flow through ionic channels in the cell membrane

Hodgkin and Huxley measured these currents and described their dynamicsthrough differential equations.

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Hodgkin-Huxley modelHodgkin-Huxley model 100

mV

0

)()()()( 43 tIEugEungEuhmgdt

duC llKKNaNa

)(

)(0

u

umm

dt

dm

m

)(

)(0

u

unn

dt

dn

n

)(

)(0

u

uhh

dt

dh

h

stimulus

NaI KI leakI

inside

outside

K

Na

Ion channels Ion pump

C glgK gNa

I

sodium potassium leakage

The likelihood the channel is open is described

by extra variables m, n, and h.

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fjtt Stimulus: EPSP

Impulse response modelImpulse response model

iuij

fjtt Stimulus: EPSP

^itt

^itt

Activation: AP

fjtt ^itt tui

j fijw

tui Firing: tti ^

linear

threshold

Activation

Previous impulse i All impulses and neurons

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Integration and activation modelIntegration and activation model

iui

)(tRIuudt

dii

tui Fire+reset

linear

threshold

Activation

resetI

j

fjtt Stimulus : EPSP

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Psychological PhenomenaPsychological Phenomena

Visual perception: viewing natural imagery we must understand ways of encodingobiects and scenes.

Spatial awareness: considering the interactionbetween streams of visual information will let us simulate concentration

Memory: modeling hippocampal structures allows us to understand various aspects of episodic memory, and learning mechanisms show how semantic memory arises.

Working memory: explaining the capacity to simultaneously hold in the mind several numbers while performing calculations requires specific mechanisms in the neural model.

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Reading words: the network will learn to read and pronounce words and then to generalize its knowledge to the pronunciation of new words as well as to recreate certain forms of dyslexia.

Semantic representations: analyzing a text on the basis of context, the appearance of individual words, the network will learn the semantics of many ideas.

Decision-making and task execution: A model of the prefrontal cortex will be able to keep attention on performed tasks in spite of hindering variables.

Development of the representation of the motor and somatosensory cortex: through learning and controlled self-organization;

Psychological PhenomenaPsychological Phenomena

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Advantages of model simulationsAdvantages of model simulationsModels help to understand phenomena: enable new inspirations, perspectives on a problem allow the simulation of effects of damages and disorders (drugs, poisoning). help to understand behavior, models can be formulated on various levels of complexity, models of phenomena overlapping in a continuous fashion (e.g. motion

or perception), models allow detailed control of experimental conditions and an exact analysis of the results Models require exact specification of underlying assumptions: allow for new predictions perform deconstructions of psychological concepts (working memory?) allow us to understand the complexity of a problem allow for simplifications enabling analysis of a complex system provide a uniform, cohesive plan of action

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Disadvantages of simulationsDisadvantages of simulations Models are often too simple, they should contain many levels. Models can be too complex, sometimes theory allows for simpler explanations (why are there no hurricanes on the equator?). It’s not always known what to provide for in a model. Even if models work, that doesn’t mean that we understand the

mechanisms Many alternative yet very different models can explain the same

phenomenon. What’s important are general rules, parameters are limited by

neurobiology on various levels; the more phenomena a model explains, the more plausible and universal it is.

Allowing for interactions and emergences (construction) is very important.

Knowledge acquired from models should undergo accumulation.

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Cognitive motivationCognitive motivation Although the thinking process seems to be sequential information processing, more detailed models predict parallel processing

Gradual transition between conscious and subconscious processes Parallel processing of sensory-motor signals by tens of millions of

neurons Specialized areas of memory responsible for various representations

e.g. shape, color, space, time Levels of symbolic representation More diffuse than binary logic

Learning mechanisms as a foundation for cognitive science When you learn, you change the method of information processing in

your brain Resonance between ”bottom-up” representation and ”top-down” understanding Prediction and competition of ideas