12:10-13:00 M,T,Th Stocker 317

Professor: Dr. Janusz Starzyk

Prereq. EE 515 or permission, Spring Quarter 1997

Text

C. Mead, "Analog VLSI and Neural Systems", Addison-Wesley, 1989.

References

B. D. Ripley, "Pattern Recognition and Neural Networks", Cambridge University Press, 1996.

Jacek M. Zurada, "Introduction to Artificial Neural Systems", West Publishing Co., 1992.

Yoh-Han Pao, "Adaptive Pattern Recognition and Neural Networks", Addison Wesley, 1989.

Course description

This course discusses basic concepts and VLSI implementation of neural networks - that is networks of elemental processors interconnected like their biological models. Neural-net implementations of pattern recognition algorithms provide important, practical advantages by allowing fast realization of parallel, iterative procedures. Operations of neural networks that are natural to VLSI design will be developed and used for different neural functions. Several examples of complete neural systems simulating biological systems will be examined.Students will simulate neural networks for patter recognition and classification using PC software tools.

Course outline

Simple, multilayered neural networks

Self organizing nets for pattern recognition

Integrated circuit synaptic connections

Active building blocks of neural networks

Circuits for elementary arithmetic functions

Analog multipliers and scalar product circuits

Associative memory implementation

Optical motion sensor

Electronic neural processors

Office hours

Tuesday and Thursday 2-3; other hours by appointment.

Office location - Stocker 347.

Grading

Grades will be based on the following;

Homework (30%)

Design project (40%)

Classroom presentation (30%)

Withdrawal

A student may withdraw from class at his discretion up to and including the first 21 days of the quarter.

Academic Conduct

Cheating, submitting work of other students as your own, or plagiarism in any form will result in penalties ranging from an F on the assignment to expulsion from the university, depending on the seriousness of the offense.