R. Ritz and T. J. Sejnowski.
Correlation coding in a stochastic network model of auditory binding.
In: Proceedings of the 3rd Joint Symposium on Neural Computation University of California, San Diego and California Institute of Technology, pages 161-171, Institute for Neural Computation, La Jolla, CA, 1996

Abstract

DeCharms, Schreiner, and Merzenich (Soc. Neurosci. Abstr. 21:1177, 1995) have provided evidence for stimulus-dependent changes in the correlations between spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmosets even when there was no change in the average firing rates. Most of the characteristics of these experimental observations can be reproduced by a simple model based on neurons having leaky integration, fire-and-reset spikes and with Poisson-distributed, balanced input. The source of synchrony in the model was common sensory input. Spike frequency adaptation was implemented by sensory-driven, delayed inhibition. The outputs of neurons in the model appear noisy (almost Poisson) owing to the stochastic nature of the input signal, but there is nevertheless a strong central peak in the correlation of the output spike trains. The experimental data and this simple model clearly demonstrate how even a noisy-looking spike train can convey basic information about a sensory stimulus in the relative spike timing between neurons. We address the binding problem and show why synchrony without periodicity might be advantageous in representing multiple objects at the same cortical site simultaneously.