Repetition suppression, neural synchronization, and behavioral priming: mechanisms underlying improved efficiency in networks of spiking neurons

Stephen J. Gotts1 and Carson C. Chow2

1Dept of Psychology and CNBC, Carnegie Mellon University
2Department of Mathematics, University of Pittsburgh

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 neural activity associated with stimulus repetition and improved performance most often decrease rather than increase - 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.