Neural Computation, Vol 11, 117-137, Copyright © 1999 by The MIT Press
Probabilistic Synaptic Transmission in the Associative Net
Bruce Graham and David Willshaw
The associative net model of heteroassociative memory with binary-valued
synapses has been extended to include recent experimental data indicating
that in the hippocampus, one form of synaptic modification is a change in
the probability of synaptic transmission. Pattern pairs are stored in the
net by a version of the Hebbian learning rule that changes the probability
of transmission at synapses where the presynaptic and postsynaptic units
are simultaneously active from a low, base value to a high, modified value.
Numerical calculations of the expected recall response of this stochastic
associative net have been used to assess the performance for different
values of the base and modified probabilities. If there is a cost incurred
with generating the difference between these probabilities, then a
difference of about 0.4 is optimal. This corresponds to the magnitude of
change seen experimentally. Performance can be greatly enhanced by using
multiple cue presentations during recall.
