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.
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.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||12 Jan 2001|
|Last Modified:||02 Mar 2012 12:39|
|Contributors:||Hertz, John (Author)
Prügel-Bennett, Adam (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||12|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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