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.


Abstract:

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.