COGNITIVE NEUROSCIENCE

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COGNITIVE NEUROSCIENCE
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Note

• Please read book to review major brain structures
  and their functions
• Please read book to review brain imaging
  techniques
• See also additional slides available on class
  website

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Cognitive Neuroscience

• the study of the relation between cognitive
  processes and brain activities
• Potential to measure some hidden processes that
  are part of cognitive theories (e.g. memory
  activation, attention, insight)
• Measuring when and where activity is happening.
  Different techniques have different strengths
  tradeoff between spatial and temporal resolution

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Techniques for Studying Brain Functioning

• Single unit recordings
• Hubel and Wiesel (1962, 1979)
• Event-related potentials (ERPs)
• Positron emission tomography (PET)
• Magnetic resonance imaging (MRI and fMRI)
• Magneto-encephalography (MEG)
• Transcranial magnetic stimulation (TMS)

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The spatial and temporal ranges of some
techniques used to study brain functioning.
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Single Cell Recording(usually in animal studies)
Measure neural activity with probes. E.g.,
research byHubel and Wiesel
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Hubel and Wiesel (1962)

• Studied LGN and primary visual cortex in the cat.
  Found cells with different receptive fields
  different ways of responding to light in certain
  areas

LGN On cell (shown on left)
LGN Off cell
Directional cell
Action potential frequency of a cell associated
with a specific receptive field in a monkey's
field of vision. The frequency increases as a
light stimulus is brought closer to the receptive
field.
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COMPUTATIONAL COGNITIVE SCIENCE
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Computer Models

• Artificial intelligence
• Constructing computer systems that produce
  intelligent outcomes
• Computational modeling
• Programming computers to model or mimic some
  aspects of human cognitive functioning. Modeling
  natural intelligence.
• ? Simulations of behavior

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Why do we need computational models?

• Provides precision need to specify complex
  theories. Makes vague verbal terms specific
• Provides explanations
• Obtain quantitative predictions
• just as meteorologists use computer models to
  predict tomorrows weather, the goal of modeling
  human behavior is to predict performance in novel
  settings

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Neural Networks

• Alternative to traditional information processing
  models
• Also known as PDP (parallel distributed
  processing approach) and Connectionist models
• Neural networks are networks of simple processors
  that operate simultaneously
• Some biological plausibility

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Idealized neurons (units)
Inputs
S
Processor
Output
Abstract, simplified description of a neuron
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Different ways to represent information with
neural networks localist representation
Unit 6
Unit 5
Unit 3
Unit 4
Unit 1
Unit 2
concept 1
concept 2
concept 3
(activations of units 0off 1on)
Each unit represents just one item ?
grandmother cells
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Coarse Coding/ Distributed Representations
Unit 6
Unit 5
Unit 3
Unit 4
Unit 1
Unit 2
concept 1
concept 2
concept 3
(activations of units 0off 1on)
Each unit is involved in the representation of
multiple items
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Advantage of Distributed Representations

• Efficiency
• Solve the combinatorial explosion problem With n
  binary units, 2n different representations
  possible. (e.g.) How many English words from a
  combination of 26 alphabet letters?
• Damage resistance
• Even if some units do not work, information is
  still preserved because information is
  distributed across a network, performance
  degrades gradually as function of damage
• (aka robustness, fault-tolerance, graceful
  degradation)

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Suppose we lost unit 6
Unit 6
Unit 5
Unit 3
Unit 4
Unit 1
Unit 2
concept 1
concept 2
concept 3
(activations of units 0off 1on)
Can the three concepts still be discriminated?
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An example calculation for a single neuron

• Diagram showing how the inputs from a number of
  units are combined to determine the overall input
  to unit-i. Unit-i has a threshold of 1 so if its
  net input exceeds 1 then it will respond with 1,
  but if the net input is less than 1 then it will
  respond with 1

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Neural-Network Models
The simplest models include three layers of
units(1) The input layer is a set of units
that receives stimulation from the external
environment. (2) The units in the input layer
are connected to units in a hidden layer, so
named because these units have no direct contact
with the environment. (3) The units in the
hidden layer in turn are connected to those in
the output layer.
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Multi-layered Networks

• Activation flows from a layer of input units
  through a set of hidden units to output units
• Weights determine how input patterns are mapped
  to output patterns
• Network can learn to associate output patterns
  with input patterns by adjusting weights
• Hidden units tend to develop internal
  representations of the input-output associations
• Backpropagation is a common weight-adjustment
  algorithm

output units
hidden units
input units
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Example of Learning Networks

• http//www.cs.ubc.ca/labs/lci/CIspace/Version3/neu
  ral/index.html

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Another example NETtalk
Connectionist network learns to pronounce English
words i.e., learns spelling to sound
relationships. Listen to this audio demo.
(after Hinton, 1989)
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Other demos

• Hopfield network
• http//www.cbu.edu/pong/ai/hopfield/hopfieldapple
  t.html
• Backpropagation algorithm and competitive
  learning
• http//www.cs.ubc.ca/labs/lci/CIspace/Version4/neu
  ral/
• http//www.psychology.mcmaster.ca/4i03/demos/demos
  .html
• Competitive learning
• http//www.neuroinformatik.ruhr-uni-bochum.de/ini/
  VDM/research/gsn/DemoGNG/GNG.html
• Various networks
• http//diwww.epfl.ch/mantra/tutorial/english/
• Optical character recognition
• http//sund.de/netze/applets/BPN/bpn2/ochre.html
• Brain-wave simulator
• http//www.itee.uq.edu.au/7Ecogs2010/cmc/home.htm
  l

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Neural Network Models

• Inspired by real neurons and brain organization
  but are highly idealized
• Can spontaneously generalize beyond information
  explicitly given to network
• Retrieve information even when network is damaged
  (graceful degradation)
• Networks can be taught learning is possible by
  changing weighted connections between nodes