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Learning synfire chains: Turning noise into signal

Learning synfire chains: Turning noise into signal
Learning synfire chains: Turning noise into signal
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.
0129-0657
445-451
Hertz, John
24c0aff2-8219-4099-a77d-c3d220242368
Prügel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Hertz, John
24c0aff2-8219-4099-a77d-c3d220242368
Prügel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

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

Record type: Article

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.

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Published date: 1996
Organisations: Southampton Wireless Group

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Local EPrints ID: 254244
URI: https://eprints.soton.ac.uk/id/eprint/254244
ISSN: 0129-0657
PURE UUID: 6646a14c-040b-40e1-8433-f04c62cb6061

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Date deposited: 12 Jan 2001
Last modified: 18 Jul 2017 09:53

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