Learning short synfire chains by self-organization

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.* This paper was presented at the Workshop on Information Theory and the Brain, held at the University of Stirling, UK, on 4–5 September 1995.