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Credit: 1 course unit
Elective course
Catalog Description:
Computational modeling and simulation of the
structure and function of brain circuits.
A short survey of the major ideas and techniques in the neural
network literature. Particular
emphasis on models of hippocampus, basal ganglia and visual cortex. A series of lab exercises introduces
techniques of neural simulation.
Prerequisites:
Permission of the instructor
Textbook(s) and/or Other Required Materials:
Papers from the literature
provided online.
Suggested additional
references:
Anders Krogh, Richard Palmer and John Hertz
(1997) Introduction to the Theory of Neural Computation. Santa Fe Institute Studies in the
Science of Complexity. Lecture
Notes, vol 1. (1991).
Christof Koch (1999) The Biophysics of Computation: Information Processing in Single
Neurons. Oxford
University Press.
Bertil Hille (1992) Ionic Channels of Excitable Membranes, 2nd edition, Sinauer Associates.
W.W. Lytton
(2002) From Computer to Brain:
Foundations of Computational Neuroscience, Springer-Verlag.
Peter Dayan and Larry
Abbott (2001) Theoretical Neuroscience: Computational and Mathematical
Modeling of Neural Systems. MIT Press
E.R. Kandel, J.H. Schwarz and T.M. Jessel
(2000) Principles of Neural Science, 4th ed., McGraw Hill.
Course Objectives and Relationship to Program
Education Objectives:
The goal of this course is
introduce students to techniques used in simulating neural systems both
at the network and the biophysical/cellular level. Students are introduced to the NEURON
(Hines and Carnevale, 1997) simulation environment. Emphasis is placed on contrasting
different modeling approaches to understanding function in the same brain
region. Objective is to give
students a view of the use of computational simulation in understanding
biological function, as well as to develop an ability to read the
neuroscience literature from a critical standpoint.
Topics Covered:
·
Neural
networks: Perceptrons, Backpropagation
·
Attractor
Dynamics (Hopfield)
·
Support
vector machines
·
Hippocampus
and Memory (Lisman, Hasselmo)
·
Cognitive
Maps (Sejnowski, Tsodyks, Rao & Sejnowski, Sakmann)
·
Neuromodulation
(Berns & Sejnowski, Durstewitz & Sejnowski, Dayan)
·
Models of
Basal Ganglia (procedural memory, reinforcement learning, categorization,
salience)
·
Texture
(Malik & Perona)
·
Salience
(Adelson & Weiss, Gilbert, Geisler, Elder)
·
Hyperacuity
(Miller & Zucker)
·
Spike-based
computation (Hopfield & Brody, Van Rullen & Thorpe)
·
Synchronization
and attention (Niebur)
·
Cortical
mechanisms of recognition (Poggio, Ullman, Hopfield)
·
Predictive
Coding (Rao & Ballard)
·
Neural
Decision Theory (Gold)
·
Similarity
and Generalization (Tenenbaum)
·
Integration
and Inference (Weiss)
Class/Laboratory Schedule:
Lecture: 3 hr/week
Lab sessions
Contribution
towards Professional Component:
100% Engineering science
Contribution
towards Program Outcomes:
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Multidisciplinary
Ability
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High
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Problem Solving
Approach
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Med.
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Problem Solving
Methods
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Med.
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Experimentation
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Med.
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Design
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Med.
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Professional
Orientation
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Med.
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Person(s) Preparing Description and Date:
Leif H. Finkel
July 2007
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