Learning Synfire Chains by Self-Organization
John Hertz and Adam Prügel-Bennett
NORDITA, Blegdamsvej 17, DK-2100 Copenhagen Ø, Denmark.
Submitted to Network
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.