Adaptation Reduces Variability of the Neuronal Population Code

TL;DR

This paper demonstrates that adaptation mechanisms in neuronal populations reduce variability in their activity, leading to more regular signals and improved decoding, supported by theoretical calculations and cortical neuron experiments.

Contribution

It introduces a master equation approach for non-renewal processes to analyze how adaptation reduces variability in neuronal population codes.

Findings

Adaptation regularizes population activity.

Enhanced post-synaptic signal decoding due to adaptation.

Theoretical results confirmed in cortical neuron experiments.

Abstract

Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for general non-renewal processes to calculate the interval and count statistics of superimposed processes governed by a slow adaptation variable. For an ensemble of spike-frequency adapting neurons this results in the regularization of the population activity and an enhanced post-synaptic signal decoding. We confirm our theoretical results in a population of cortical neurons.