Path: utzoo!utgpu!water!watmath!clyde!att!osu-cis!tut.cis.ohio-state.edu!mailrus!ames!haven!mimsy!oddjob!tank!uxc!ksuvax1!cseg!lag From: lag@cseg.uucp (L. Adrian Griffis) Newsgroups: comp.ai.neural-nets Subject: Re: temporal domain in vision Keywords: multiplex filter model Message-ID: <724@cseg.uucp> Date: 21 Sep 88 20:50:53 GMT References: <233@uceng.UC.EDU> Organization: College of Engineering, University of Arkansas, Fayetteville Lines: 29 In article <233@uceng.UC.EDU>, dmocsny@uceng.UC.EDU (daniel mocsny) writes: > In Science News, vol. 134, July 23, 1988, C. Vaughan reports on the > work of B. Richmond of NIMH and L. Optican of the National Eye > Institute on their multiplex filter model for encoding data on > neural spike trains. The article implies that real neurons multiplex > lots of data onto their spike trains, much more than the simple > analog voltage in most neurocomputer models. I have not seen > Richmond and Optican's papers and the Science News article was > sufficiently watered down to be somewhat baffling. Has anyone > seen the details of this work, and might it lead to a method to > significantly increase the processing power of an artificial neural > network? My understanding is that neurons in the eye depart from a number of general rules that neurons seem to follow elsewhere in the nervous system. One such departure is that sections of a neuron can fire independent of other sections. This allows the eye to behave as though is has a great many logical neuron without having to use the the space that the same number of discrete cellular metabolic systems would require. I'm not an expert in this field, but this suggests to me that many of the special tricks that neurons of the eye employ may be attempts to overcome space limitations rather than to make other processing schemes possible. Whether or not this affects the applicability of such tricks to artificial neural networks is another matter. After all, artificial neural networks have space limitations of their own. -- UseNet: lag@cseg L. Adrian Griffis BITNET: AG27107@UAFSYSB