On the mathematical consequences of binning spike trains

TL;DR

This paper analyzes how binning spike trains affects their statistical properties, showing that binning transforms Markov processes into Variable Length Markov Chains and relates their distribution to Gibbs measures, impacting neural prediction and criticality detection.

Contribution

It provides a mathematical framework demonstrating that binning induces a transition from Markov to Variable Length Markov Chains and relates binned spike trains to Gibbs measures, offering insights into neural prediction and criticality.

Findings

Binning converts Markov spike trains into Variable Length Markov Chains.

The law of binned spike trains can be described as a Gibbs measure.

Binning influences the interpretation of neural prediction and criticality detection.

Abstract

We initiate a mathematical analysis of hidden effects induced by binning spike trains of neurons. Assuming that the original spike train has been generated by a discrete Markov process, we show that binning generates a stochastic process which is not Markov any more, but is instead a Variable Length Markov Chain (VLMC) with unbounded memory. We also show that the law of the binned raster is a Gibbs measure in the DLR (Dobrushin-Lanford-Ruelle) sense coined in mathematical statistical mechanics. This allows the derivation of several important consequences on statistical properties of binned spike trains. In particular, we introduce the DLR framework as a natural setting to mathematically formalize anticipation, i.e. to tell "how good" our nervous system is at making predictions. In a probabilistic sense, this corresponds to condition a process by its future and we discuss how binning may affect our conclusions on this ability. We finally comment what could be the consequences of binning in the detection of spurious phase transitions or in the detection of wrong evidences of criticality.