REPETITION SUPPRESSION, NEURAL SYNCRHONIZATION, AND BEHAVIORAL PRIMING:
MECHANISMS UNDERLYING IMPROVED EFFICIENCY IN NETWORKS OF SPIKING NEURONS
Stephen J. Gotts (1, 2) and Carson C. Chow (1, 3)
(1) Center for the Neural Basis of Cognition, Pittsburgh, PA
(2) Department of Psychology, Carnegie Mellon University
(3) Department of Mathematics, University of Pittsburgh
Click here to
view an electronic version of the poster.
Human subjects tend to perform tasks faster and more accurately with
practice. Cognitive-level explanations of these behavioral changes often
involve some form of threshold modification to node activity or changes in
connection strengths such that representations become active faster, at
higher levels, and/or with greater precision.
However, the changes in neural activity associated with stimulus
repetition and improved performance are most often decreases rather than
increases - a phenomenon known as "repetition suppression" (Desimone,
1996). We propose that the behavioral improvement following stimulus
repetition involves greater neural synchronization and more efficient
neural processing that arises from a reduction of activity.
We show that artificial networks of spiking neurons with "synaptic
depression" (an automatic reduction in synaptic efficacy following
pre-synaptic activity), can account for many of the empirical findings
associated with repetition suppression in humans and monkeys. Synaptic
depression leads to reductions in both the mean and variance of neural
firing rates which dynamically enhances neural synchronization.
As neurons synchronize, processing efficiency increases because fewer
spikes are required to fire post-synaptic neurons. This can improve the
rate of information transmission, allowing earlier propagation of
individual spikes throughout an entire processing pathway.
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This page last updated 28 March 2001.