Information processing by the brain

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Information processing by the brain
Computational Intelligence
Based on a course taught by Prof. Randall
O'Reilly University of Colorado and Prof.
Wlodzislawa Ducha Uniwersytet Mikolaja Kopernika
Janusz A. Starzyk
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Basic mechanisms

• Microorganization 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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Rules

• 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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Macrolevel

• 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 approximate
• differentiation, yielding representation
  invariance, specialization and hierarchy.

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Hierarchy and specialization

• Mental 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 interaction

• Specialization 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 principles

• Well-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 gt local context
  (internal) changes the interpretation.
• Time flies like an arrow
• Fruit 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 cortex
Four cortical lobes and their functions
Brodmann's areas of the cortex
Various terms used to refer to locations in the
brain
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General 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 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 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 areas
Basal ganglia (striatum, globus pallidus,
substantia nigra) Basal ganglia initiate motor
activities and the substantia nigra is
responsible for controlling learning

• 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.

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3 principle brain areas

• Posterior 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 learning

• A 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 gt 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 memory

• Distributed 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 gt 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 architecture

• Hierarchical structure for sensory data,
  recurrence in FC, recording the context.

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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 action

• Automatic 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 - 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 gt 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 problems

• There 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 gt 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 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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Challenges

• 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...