Cortical Spike Synchrony as a Measure of Input Familiarity

TL;DR

This paper proposes that cortical spike synchrony reflects the brain's estimate of input familiarity, serving as a rapid indicator of how well incoming stimuli match prior experiences, based on network structure.

Contribution

It introduces a model where spike synchrony encodes prior probability of stimuli, offering a new interpretation of synchrony as a measure of input familiarity rather than just stimulus binding.

Findings

Networks can produce stimulus-dependent spike synchrony.

Synchrony indicates high prior probability of input patterns.

Model explains experimental observations of cortical synchrony.

Abstract

Spike synchrony, which occurs in various cortical areas in response to specific perception, action and memory tasks, has sparked a long-standing debate on the nature of temporal organization in cortex. One prominent view is that this type of synchrony facilitates the binding or grouping of separate stimulus components. We argue instead for a more general function: A measure of the prior probability of incoming stimuli, implemented by long-range, horizontal, intra-cortical connections. We show that networks of this kind -- pulse-coupled excitatory spiking networks in a noisy environment -- can provide a sufficient substrate for stimulus-dependent spike synchrony. This allows a quick (few spikes) estimate of the match between inputs and the input history as encoded in the network structure. Given the ubiquity of small, strongly excitatory subnetworks in cortex, we thus propose that many experimental observations of spike synchrony can be viewed as signs of input patterns that resemble long-term experience, i.e. patterns of high prior probability.