| |
Abstract:
We analyze the conditions under which synaptic learning rules
based on action potential timing can be approximated by learning
rules based on firing rates. In particular, we consider a form of
plasticity in which synapses depress when a presynaptic spike is
followed by a postsynaptic spike, and potentiate with the opposite
temporal ordering. Such {\em differential anti-Hebbian plasticity}
can be approximated under certain conditions by a learning rule
that depends on the time derivative of the postsynaptic firing
rate. We argue that this learning rule acts to stabilize persistent
neural activity patterns in recurrent neural networks, and
illustrate its operation with numerical simulations of an
autapse.
|
