Statistical Neuroscience in the Single Trial Limit

TL;DR

This paper reviews recent statistical methods that analyze neural activity in single trials by exploiting structural assumptions, enabling insights into neural circuit operation without relying on repeated trials.

Contribution

It summarizes advances in statistical modeling techniques that handle trial-limited neural data by leveraging simplifying structures like shared gain and temporal smoothness.

Findings

Identification of shared gain modulations across neural populations

Use of temporal smoothness to analyze neural firing rates

Exploitation of response correlations across behavioral conditions

Abstract

Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cognitive and behavioral state. Disentangling these sources of variability is of great scientific interest in its own right, but it is also increasingly inescapable as neuroscientists aspire to study more complex and naturalistic animal behaviors. In these settings, behavioral actions never repeat themselves exactly and may rarely do so even approximately. Thus, new statistical methods that extract reliable features of neural activity using few, if any, repeated trials are needed. Accurate statistical modeling in this severely trial-limited regime is challenging, but still possible if simplifying structure in neural data can be exploited. We review recent works that have identified different forms of simplifying structure -- including shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions -- and exploited them to reveal novel insights into the trial-by-trial operation of neural circuits.