CS 564 : Fall 1999

Brain Theory and Artificial Intelligence

 

Tu Th: 11-12:20 am; OHE 100

Instructor: Prof. Michael A. Arbib; HNB-03, (213) 740-9220, arbib@pollux.usc.edu.

(Office hours: 11-12 Wednesdays, HNB 03.)

Teaching Assistants: Erhan Oztop, erhan@java.usc.edu, Salvador Marmol, smarmol@rana

This course provides a basic understanding of brain function, of the artificial neural networks which provide tools for a new paradigm for adaptive parallel computation, and of the Neural Simulation Language NSLJ which allows us to study biological and artificial neural networks in great detail. No background in neuroscience is required, nor is specific programming expertise, but knowledge of Java will enable students to extend the NSLJ functionality in interesting ways.

The Brain as a Network of Neurons

Perceptual and Motor Schemas

Neural Modeling in Perspective – Connectionism and Cognitive Science

Didday Model of Winner-Take-All

Introduction to NSL: modules; SCS schematic Capture System; The window interface and graphics

Hopfield Networks, Constraint Satisfaction, and Optimization

[NSLJ] Maxselector: a. How to run the model; b. How to write the model

[NSLJ] Hopfield: a. How to run the model; b. How to write the model

Adaptive Networks – Hebbian learning, Perceptrons;
[NSLJ] Landmark Learning

Adaptive Networks – Gradient Descent and Backpropagation

[NSLJ] Backprop: a. How to run the model; b. How to write the model

The Modeling Language NSLM; The Scripting Language NSLS

Midterm

 

Systems Concepts

Feedback and the Spinal Cord; [NSLJ]

The FARS model of Control of Reaching and Grasping 1

The FARS model of Control of Reaching and Grasping 2

Review of Midterm and NSL

[NSLJ] Control of Saccades – Dominey 1

[NSLJ] Control of Saccades – Dominey 2

5pm-6:20pm – Make-up midterm

Hedco Neurosciences Building: 10th Anniversary Celebration

Visual Preprocessing; Lateral inhibition; Von Bekésy model

Depth Perception; [NSLJ] Dev and House Models

Self-Organizing Feature Maps; [NSLJ] Kohonen Maps

Competition and Cooperation in Neural Nets

Holiday: Thanksgiving Day

Reinforcement Learning and Motor Control

Robotic Learning

[NSLJ] Cerebellar Adaptation of Movement Generation -- Prism Adaptation Model

Brain Models on the Web (BMW)

 

Texts:
A. Weitzenfeld, M.A. Arbib and A. Alexander, 1999-2000, NSL Neural Simulation Language, MIT Press (in press).

M.A. Arbib, Ed., 1995, The Handbook of Brain Theory and Neural Networks, MIT Press (paperback).

Supplementary reading: M.A. Arbib, 1989, The Metaphorical Brain 2: Neural Networks and Beyond, Wiley-Interscience.

One mid-term and a final will cover the entire contents of the readings as well as the lectures.

The final exam will cover all of the course, but emphasizing material not covered in the mid-term.

Distribution of Grades: NSL assignments and other homework or projects: 40%; Mid-term: 25%; Final Exam: 35%.

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