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Journal of Neuroscience, Vol 13, 3406-3420, Copyright © 1993 by Society for Neuroscience
A spiking network model of short-term active memory
D Zipser, B Kehoe, G Littlewort and J Fuster
Department of Cognitive Science, University of California, San Diego, La Jolla 92093.
Studies of cortical neurons in monkeys performing short-term memory tasks
have shown that information about a stimulus can be maintained by
persistent neuron firing for periods of many seconds after removal of the
stimulus. The mechanism by which this sustained activity is initiated and
maintained is unknown. In this article we present a spiking neural network
model of short-term memory and use it to investigate the hypothesis that
recurrent, or "re-entrant," networks with constant connection strengths are
sufficient to store graded information temporarily. The synaptic weights
that enable the network to mimic the input-output characteristics of an
active memory module are computed using an optimization procedure for
recurrent networks with non-spiking neurons. This network is then
transformed into one with spiking neurons by interpreting the continuous
output values of the nonspiking model neurons as spiking probabilities. The
behavior of the model neurons in this spiking network is compared with that
of 179 single units previously recorded in monkey inferotemporal (IT)
cortex during the performance of a short-term memory task. The spiking
patterns of almost every model neuron are found to resemble closely those
of IT neurons. About 40% of the IT neuron firing patterns are also found to
be of the same types as those of model neurons. A property of the spiking
model is that the neurons cannot maintain precise graded activity levels
indefinitely, but eventually relax to one of a few constant activities
called fixed-point attractors. The noise introduced into the model by the
randomness of spiking causes the network to jump between these attractors.
This switching between attractor states generates spike trains with a
characteristic statistical temporal structure. We found evidence for the
same kind of structure in the spike trains from about half of the IT
neurons in our test set. These results show that the behavior of many real
cortical memory neurons is consistent with an active storage mechanism
based on recurrent activity in networks with fixed synaptic strengths.
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