Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Posting-Version: version B 2.10.2 9/18/84; site lasspvax.UUCP Path: utzoo!watmath!clyde!cbosgd!ihnp4!houxm!vax135!cornell!lasspvax!norman From: norman@lasspvax.UUCP (Norman Ramsey) Newsgroups: net.math Subject: Neural Net COmputing Message-ID: <663@lasspvax.UUCP> Date: Mon, 11-Nov-85 19:09:55 EST Article-I.D.: lasspvax.663 Posted: Mon Nov 11 19:09:55 1985 Date-Received: Wed, 13-Nov-85 07:34:02 EST Reply-To: norman@lasspvax.UUCP (Norman Ramsey) Organization: LASSP, Cornell University Lines: 22 Summary: A model of human memory may lead to some interesting devices I recently heard a talk given here on Hopfield memories and neural network devices. The work I heard about is being done at Bell Labs by Larry Jaeckel's group. The idea is fairly simple: you take N "neurons", connect each to all the others, and let the firing rate of a given neuron depend on the stimuli on its inputs, which can be excitatory or inhibitory. Jaeckel's people are using op amps with resistors and capacitors, where voltage is the quantity analogous to firing rate, and conductance is analogous to the transmittivity (or whatever) of a synapse. Apparently they have been able to make an associative memory out of these gadgets, and have also taken a good crack at the traveling salesman problem (by letting the device minimize energy). Does anyone know more about the mathemtics of these things? How many elements can be stored in such an associative memory? What are expected error rates like? What are the possibilities for programming or calculating with these devices? -- Norman Ramsey ARPA: norman@lasspvax -- or -- norman%lasspvax@cu-arpa.cs.cornell.edu UUCP: {ihnp4,allegra,...}!cornell!lasspvax!norman