Entropy and Information in Neural Spike Trains

TL;DR

This paper introduces a method to quantify information in neural spike trains based solely on spike timing, and applies it to fly visual neurons to analyze their information encoding.

Contribution

It presents a novel approach to measure information in spike trains without assumptions about specific features or input waveforms.

Findings

Quantifies information in spike timing in bits.

Applied method to fly visual neuron data.

Provides insights into neural encoding of motion.

Abstract

The nervous system represents time-dependent signals in sequences of discrete action potentials or spikes, all spikes are identical so that information is carried only in the spike arrival times. We show how to quantify this information, in bits, free from any assumptions about which features of the spike train or input waveform are most important. We apply this approach to the analysis of experiments on a motion-sensitive neuron in the fly visual system.