Cognitive Neuroscience

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Cognitive Neuroscience
Computational Intelligence
Based on a course taught by Prof. Randall
O'Reilly University of Colorado, Prof.
Wlodzislaw Duch Uniwersytet Mikolaja
Kopernika and http//wikipedia.org/
http//grey.colorado.edu/CompCogNeuro/index.php/CE
CN_CU_Boulder_OReilly http//grey.colorado.edu/Com
pCogNeuro/index.php/Main_Page
Janusz A. Starzyk
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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 wont help much.

• How can we understand the workings of a TV or
  computer?
• Experiments wont 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 anything?

• An important question how do we know things?
• Example super diet based on dr. K, Chinese
  medicine
• and other miracle methods. How do we know that
• they work? How do we know that they are for real?

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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 brains functions We can answer
many questions only from an ecological and
evolutionary perspective why is the world the
way it is? Because thats 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 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.
10-4 m, small networks synchronization of neuron
activity, recurrence, neurodynamics, multistable
systems, pattern generators, memory, chaotic
behaviors, neural encoding neurophysiology ...
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.
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to behavior
10-2 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 description
Summary (Churchland, Sejnowski 1988)
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How does it all work?
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Systemic level
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to 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 gt finished
  machines gt behavior

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A 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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Model of transformation
Agent Architecture
Reason
Short-term Memory
Perceive
Act
RETRIEVAL
LEARNING
Long-term Memory
INPUT
OUTPUT
Task
Environment
Simulation or Real-World System
From Randolph M. Jones, P www.soartech.com
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Model of self-organization

• Topographical representations in numerous areas
  of the brainsensory 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.
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Dynamic model

• Strong feedback, neurodynamics.
• Hopfield model associative memory, learning
  based on Hebbs law, synchronized dynamics,
  two-state neurons.

Vector of input potentials V(0)Vini , i.e. input
output. Dynamics (iterations) Þ Hopfields
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.
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Biophysical model spiking neurons
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 foundations
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 dynamics through
differential equations.
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Hodgkin-Huxley model
inside
K
Na
outside
Ion channels
Ion pump
sodium potassium leakage
The likelihood the channel is open is described
by extra variables m, n, and h.
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Impulse response model
Activation
i
Activation AP
All impulses and neurons
Previous impulse i
linear
threshold
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Integration and activation model
Activation
i
reset
I
Stimulus EPSP
linear
Firereset
threshold
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Psychological Phenomena

• Visual perception viewing natural imagery
• we must understand ways of encoding
• obiects and scenes.
• Spatial awareness considering the interaction
• between 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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Psychological Phenomena

• 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
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Advantages of model simulations

• Models 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 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?).
• Its not always known what to provide for in a
  model.
• Even if models work, that doesnt mean that we
  understand the mechanisms
• Many alternative yet very different models can
  explain the same phenomenon.
• Whats 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 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