Population rate codes carried by mean, fluctuation and synchrony of neuronal firings

TL;DR

This study investigates how neuronal populations encode information through mean firing rate, fluctuation, and synchrony, using a generalized rate-code model and analysis methods to reveal independent information channels.

Contribution

It introduces a comprehensive rate-code model incorporating noise and demonstrates that firing rate, fluctuation, and synchrony can independently carry information.

Findings

AMM results agree with direct simulations

ICA shows independent information channels

Higher sensitivity in input-output relation with increased multiplicative noise

Abstract

A population of firing neurons is expected to carry information not only by mean firing rate but also by fluctuation and synchrony among neurons. In order to examine this possibility, we have studied responses of neuronal ensembles to three kinds of inputs: mean-, fluctuation- and synchrony-driven inputs. The generalized rate-code model including additive and multiplicative noise (H. Hasegawa, Phys. Rev. E {\bf 75} (2007) 051904) has been studied by direct simulations (DSs) and the augmented moment method (AMM) in which equations of motion for mean firing rate, fluctuation and synchrony are derived. Results calculated by the AMM are in good agreement with those by DSs. The independent component analysis (ICA) of our results has shown that mean firing rate, fluctuation (or variability) and synchrony may carry independent information in the population rate-code model. The input-output relation of mean firing rates is shown to have higher sensitivity for larger multiplicative noise, as recently observed in prefrontal cortex. A comparison is made between results obtained by the integrate-and-fire (IF) model and our rate-code model.