Revealing stimulus-dependent dynamics through statistical complexity

TL;DR

This study demonstrates that statistical complexity, an information-theoretic measure, can effectively reveal stimulus-dependent neural population dynamics across different brain regions, surpassing traditional variability metrics.

Contribution

The paper introduces the use of statistical complexity to identify stimulus-specific neural activity patterns across multiple brain areas, providing new insights into neural dynamics.

Findings

Statistical complexity detects stimulus-specific motifs in neural activity.

Coefficient of variation fails to distinguish different stimulus conditions.

Neural population dynamics exhibit structured, stimulus-dependent patterns.

Abstract

Advances in large-scale neural recordings have expanded our ability to describe the activity of distributed brain circuits. However, understanding how neural population dynamics differ across regions and behavioral contexts remains challenging. Here, we surveyed neuronal population dynamics across multiple mouse brain areas (visual cortex, hippocampus, thalamus, and midbrain) using spike data from local ensembles. Two complementary measures were used to characterize these dynamics: the coefficient of variation (CV), a classical indicator of spike-time variability, and statistical complexity, an information-theoretic quantifier of organizational structure. To probe stimulus-dependent activity, we segmented and concatenated recordings from behavioral experiments into distinct time series corresponding to natural image presentations, blank screens during visual task, and spontaneous activity. While the CV failed to discriminate between these conditions, statistical complexity revealed clear, stimulus-specific motifs in population activity. These results indicate that information-theoretic measures can uncover structured, stimulus-dependent patterns in neural population dynamics that remain unobserved in traditional variability metrics.