COE%PLU@ames-io.ARPA (02/02/88)
*********************** OPEN TECHNICAL MEETING ***********************
IEEE Computer Society, Santa Clara Valley Chapter
Tuesday Febuary 9, 1988 8:00 p.m.
Hewlett-Packard Cupertino (Wolfe & Homestead)
Building 48, Oak Room
"CAPACITY FOR PATTERNS AND SEQUENCES IN KANERVA'S SDM AS COMPARED TO
OTHER ASSOCIATIVE MEMORY MODELS"
ABSTRACT: Dr. James Keeler of Stanford University will be speaking on the
information capacity of Kanerva's Sparse, Distributed Memory
(SDM) and Hopfield-type neural networks will be discussed. Using
certain approximations, it is shown that the total information
stored in these systems is proportional to the number connections
in the network. The proportionality constant is the same for the
SDM and Hopfield-type models independent of the particular model,
or the order of the model. This same analysis can be used to show
that the SDM can store sequences of spatiotemporal patterns, and
the addition of time-delayed connections allows the retrieval of
context dependent temporal patterns with varying time delays.
Dr. Keeler Received his Ph.D in Physics from U. C. San Diego, March 1987.
His dissertation was on reaction-diffusion systems and neural network models.
He is now a Postdoctoral student at Stanford University's Department of
Chemistry working on neural network models, as well as consulting for Penti
Kanerva's (RIACS, NASA Ames Research Center) SDM research group.
For additional information contact Coe Miles-Schlichting:
coe@pluto.arc.nasa.gov or (408) 279-4773