Feature selection in simple neurons: how coding depends on spiking dynamics

TL;DR

This paper investigates how a neuron's feature selectivity, as measured by spike-triggered averages, depends on model parameters and input statistics, shedding light on coding strategies in simple neuron models.

Contribution

It demonstrates how feature selectivity varies with input statistics and model parameters in simple neuron models, linking coding strategies to input dynamics.

Findings

Feature selectivity depends on input statistics.

Model parameters influence spike-triggered averages.

Coding strategies adapt to input changes.

Abstract

The relationship between a neuron's complex inputs and its spiking output defines the neuron's coding strategy. This is frequently and effectively modeled phenomenologically by one or more linear filters that extract the components of the stimulus that are relevant for triggering spikes, and a nonlinear function that relates stimulus to firing probability. In many sensory systems, these two components of the coding strategy are found to adapt to changes in the statistics of the inputs, in such a way as to improve information transmission. Here, we show for two simple neuron models how feature selectivity as captured by the spike-triggered average depends both on the parameters of the model and on the statistical characteristics of the input.