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Information processing Information processing by the brainby the brain

Janusz A. Starzyk

Computational IntelligenceComputational Intelligence

Based on a course taught by Prof. Randall O'Reilly University of Colorado and Prof. Włodzisława DuchaUniwersytet Mikołaja Kopernika

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Basic mechanismsBasic mechanismsMicroorganization: basic rules, similar in the whole brain. Macroorganization: diversification and interactions of different areas. On the micro level in the Leabra model we have 6 rules:

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RulesRules The brain is not a universal computer. Neurons adjusted evolutionally to detect specific properties of

analyzed signals. Compromise between specificity and built-in expectations, and

generality and universality. Compromise between speed of the hippocampus representing

temporal sequences, and slowness of the cortex integrating many events.

Compromise between active memory and control of understanding.

How to build, using neurons, all necessary elements - specific and universal?

Dynamic rules on the macro level: Constraint satisfaction (including internal), knowledge a priori. Contrast reinforcement, attractors, active memory. Attention mechanisms, inhibitory competition.

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MacrolevelMacrolevel

Neuron-detector layers strengthening/weakening differences. Hierarchical transformation sequences. Special transformations for different signals. Specialized information transfer pathways. Interactions within pathways. Processing and memory built into the same hardware Higher-level association areas. Distributed representations across large areas.Strong feedback between areas causes this to be only approximatedifferentiation, yielding representation invariance, specialization and

hierarchy.

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Hierarchy and specializationHierarchy and specializationMental processes: the result of hierarchical and specialized

transformation of sensory signals, internal states (categories) and undertaken actions.

Neuron-detector layers process signals coming to them from receptors, strengthening/weakening differences.

Emerging internal states provide interpretations of environmental states - hierarchical processing is necessary to attain invariant representations, despite variable signals, eg. aural (phonemes), or visual (colors, objects).

Transformations and specialized information processing streams stimulate internal representations of categories and provide data for taking action, e.g. motor reactions. Simultaneously, processed information modifies the means of information processing.

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Distribution and interactionDistribution and interactionSpecialization increases efficiency of activity, but interactions between streams are essential for coordination, acquiring additional stable information on different levels, e.g.. spatial orientation and object recognition.

On a higher level we have heterogenic association areas.

Knowledge linked to recognition (e.g. reading words) is distributed across the whole brain, creating a semantic memory system.

It's similar on a micro and macro level: interpretation of the whole is the result of distributed activity of many elements.

Knowledge = processing,

Program ~ data.

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Dynamic principlesDynamic principlesWell-known inputs trigger an immediate reaction.New ones may require iterative searches for the best compromise satisfying constraints resulting from possessed knowledge = possible to attain dynamic states of the brain. There exist many local, alternative or sub-optimal, solutions => local context (internal) changes the interpretation.

Time flies like an arrowFruit flies like a banana

Long-term memory is the result of learning, this is synaptic memory.

Active memory (dynamic) is the result of momentary mutual activations of active areas; it's short-term because the neurons get tired and are involved in many processes; this directly influences processes in other areas of the brain.

This mechanism causes the non-repeatability of experiences = internal interpretations, contextual states are always somewhat diverse.

Concentration is the result of inhibitory interactions.

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General functions of the cortexGeneral functions of the cortex

Brodmann's areas of the cortex

Four cortical lobes and their functions

Various terms used to refer to locations in the brain

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General functions of the cortexGeneral functions of the cortex Four lobes of the cortex:     frontal lobe     occipital lobe     parietal lobe     temporal lobe

The frontal lobe is responsible for: planning, thinking, memory, willingness to act and make decisions, evaluation of emotions and situations, memory of learned motor actions, e.g. dance, mannerisms, specific patterns of behavior, words, faces, predicting consequences, social conformity, tact, feelings of serenity (reward system), frustration, anxiety and stress. The occipital lobe is responsible for: sight, analyzing colors, motion, shape, depth, visual associations

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General functions of the cortexGeneral functions of the cortex

     parietal lobe     temporal lobe  

The parietal lobe is responsible for: spatial orientation, motion recognition, feeling temperature, touch, pain, locating sensory impressions, integration of motion, sensation and sight, understanding abstract concepts. The temporal lobe is responsible for: speech, verbal memory, object recognition, hearing and aural impressions, scent analysis.

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Subcortical areasSubcortical areas

Brain stem:

raphe nuclei: serotonin,

reticular formation: general

consciousness.

Midbrain: (mesencephalon):

part of the ventral tegmental

area (VTA): dopamine,

value of observation/action.

Thalamus: input of sensory signals, attention

Cerebellum: learning motion, temporal sequences of motion.

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Subcortical areasSubcortical areas

Amygdala: emotions, affective associations. Basal ganglia: sequences, anticipation, motor

control, modulation of prefrontal cortex activity,

selection and initiation of new activity. Hippocampus: fast learning, episodic and spatial

memory.

Basal ganglia (striatum, globus pallidus, substantia nigra)Basal ganglia initiate motor activities and the substantia nigra is responsible for controlling learning

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3 principle brain areas3 principle brain areasPosterior cortex PC – rear parietal cortex and motor cortex; sensorymotor actions, specialization, distributed representations

Frontal cortex FC – prefrontal cortex, higher cognitive behaviors, isolated representations

Hippocampus HC – hippocampus and related structures, memory, rapid learning, sparse representations.

Learning must be slow in order to grasp statistically important relationships, and

to precisely analyze sensory data and control motions, but we also need a

mechanism for rapid learning. Compromise: slow learning in the cortex and rapid learning in the hippocampus.Retaining active information and simultaneously accepting new information in a

distributed system, avoiding interference.

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Slow/rapid learningSlow/rapid learningA neuron learns conditional

probability, the correlation

between desired activity and

input signals; the optimal value

of 0.7 is reached quickly only

with a small learning constant of

0.005

Every experience is a small fragment of uncertain, potentially useful knowledge

about the world => stability of one's image of the world requires slow learning,

integration leads to forgetting individual events. We learn important new information after one exposure. Lesions of the hippocampus trigger follow-up amnesia. The system of neuromodulation reaches a compromise between stability and

plasticity.

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Active memoryActive memoryDistributed overlapping representations in the PC can

efficiently record information about the world, but...

having too many associations and connections

decreases the possibility of precise discovery of

information, it can also blur it with the passage of time.

FC – prefrontal cortex, stores isolated representations;

greater memory stability.

Inhibition => active memory must be selective, the effect is a focusing

of attention.

Attention is not a result of the activity of separate mechanisms

connected with the will, it's an emergent process resulting from the

necessity of fulfilling many constraints simultaneously.

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Cognitive architectureCognitive architecture

Hierarchical structure for sensory data, recurrence in FC, recording

the context.

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Activity Activity

Parietal cortex: learns slowly, creates extensive, overlapping

representations in a densely connected network.

Dynamic PC states are short-term memory, mainly of spatial relations,

quickly yielding to disorder and disintegration.

Frontal cortex: learns slowly, stores isolated representations, activation

of memory is more stable, the reward mechanism dynamically switches

its activity, allowing a longer active memory.

The hippocampus learns quickly, creating sparse representations,

differentiating even similar events.

This simplified architecture will allow the modeling of many

phenomena relevant to perception, memory, using language, and the

effects of the interaction of different areas.

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Controlled/automatic actionControlled/automatic actionAutomatic: routine, simple, low level, sensory-motor, conditional reflexes, associations – easy to model with a network.

Controlled: conscious, elastic, requiring sequences of actions, selection of elements from a large set of possibilities – usually realized in a descriptive way with the help of systems of rules and symbols.

Models postulating central processes: like in a computer, working memory with a central monitor, having influence over many areas.

Here: emergent processes, the result of global constraint fulfillment, lack of a central mechanism.

The prefrontal cortex can exert control over the activity of other areas, so it's involved in controlled actions, including the representation of "me" vs. "others", social relationships etc.

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Other distinctions - consciousnessOther distinctions - consciousness Declarative vs. procedural knowledge

Declarative: often expressed symbolically (words, gestures). Procedural: more oriented towards sequences of actions.

Explicit vs. implicit knowledge

Controlled action relies on explicit and declarative knowledge.Automatic actions rely on implicit and procedural knowledge.

Consciousness => states existing for a noticeable period of time, integrating reportable sensory information about different modalities, with an influence on other processes in the brain.

Each system, which has internal states and is complex enough to comment on them, will claim that it's conscious.

Processes in the prefrontal cortex and the hippocampus can be recalled as a brain state or an episode, can be interpreted

(associated with concept representation).

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Various potential problemsVarious potential problemsThere are easy things, for which simple models will suffice, and difficult things requiring detailed models.

Many misunderstandings: MLP neural networks are not brain models, they are only loosely inspired by a simplified look at the activity of neural networks; an adequate neural model must have appropriate architecture and rules of learning.

Example: catastrophic forgetting of associations from lists, much stronger in MLP networks than in people => appropriate architecture, allowing for two types of memory (hippocampus + cortex) doesn't have a problem with this.

Human cognition is not perfect and good models allow us to analyze the numerous compromises handled by the brain.

Brains are fairly elastic, although they mostly base their actions on the

representation of specific knowledge about the world.

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Problem of integrationProblem of integration Binding problem: we perceive the

world as a whole, but information in

the brain, after initial processing,

doesn't descend anywhere. Likely synchronization of distributed

processes. Attention is a control mechanism

selecting areas which should be

active in a given moment. Encoding relevant combinations of

active areas.

Simultaneous activity = dynamic synchronization, partial reconstruction

of the brain state during an episode.

Integration errors happen often.

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ChallengesChallenges Disruptions: Multi-level transition from one activity to another and

back to the first, or recurrent multiple repetition of the same activity. This is easy for a computer program (loops, subroutines), where

data and programs are separated, but it's harder for a network,

where there is no such separation. PFC and HCMP remember the previous state and return to it. Difficult task, we often forget what we wanted to say when we listen

to someone, sentences are not nested too deeply.

The rat the cat the dog bit chased squeaked.

How and what should be generalized? Distributed representations

connect different features.

Dogs bite, and not only Spot, not only mongrels, not only black dogs...