Sequential Detection of Regime Changes in Neural Data

TL;DR

This paper addresses the challenge of detecting regime changes in neural firing patterns by reviewing existing algorithms and introducing a new technique, validated on mouse neural data to identify behavioral learning.

Contribution

It presents a novel algorithmic approach for detecting deviations from baseline neural activity, applicable to spike and local field potential data.

Findings

Algorithms successfully detect behavioral learning in mouse neural data

New technique improves detection of regime changes

Applicable to various neural data types

Abstract

The problem of detecting changes in firing patterns in neural data is studied. The problem is formulated as a quickest change detection problem. Important algorithms from the literature are reviewed. A new algorithmic technique is discussed to detect deviations from learned baseline behavior. The algorithms studied can be applied to both spike and local field potential data. The algorithms are applied to mice spike data to verify the presence of behavioral learning.