Learning synfire chains: Turning noise into signal


Hertz, John and Prügel-Bennett, Adam (1996) Learning synfire chains: Turning noise into signal. International Journal of Neural Systems, 7, (4), 445-451.

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Description/Abstract

A model of cortical neurons capable of sustaining a low level of spontaneous activity is investigated. Without learning the activity of the network is chaotic. We report on attempts to learn synfire chains in this type of network by introducing a Hebbian learning mechanism and exciting a small set of neurons at random intervals. We discuss the types of instabilities that can arise and prevent the formation of long synfire chains and also discuss various biologically plausible mechanisms which to some extent cure these instabilities.

Item Type: Article
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 254244
Date Deposited: 12 Jan 2001
Last Modified: 27 Mar 2014 19:57
Further Information:Google Scholar
ISI Citation Count:12
URI: http://eprints.soton.ac.uk/id/eprint/254244

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