Paper List

期刊: ArXiv Preprint
发布日期: 2025-12-04
NeuroscienceComputational Biology

Revealing stimulus-dependent dynamics through statistical complexity

Universidade Federal de Pernambuco | University of Minho | University of Arkansas | Universidade Federal de Alagoas

Edson V. de Paula, Rafael M. Jungmann, Antonio J. Fontenele, Leandro A. A. Aguiar, Pedro V. Carelli, Fernanda S. Matias, Mauro Copelli, Nivaldo A. P. de Vasconcelos
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30秒速读

IN SHORT: This paper addresses the core challenge of detecting stimulus-specific patterns in neural population dynamics that remain hidden to traditional variability metrics like the coefficient of variation.

核心创新

  • Methodology Introduces the application of statistical complexity, an information-theoretic measure based on ordinal pattern analysis (Bandt-Pompe symbolization), to characterize the organizational structure of neural population dynamics across multiple brain regions.
  • Biology Reveals a hierarchical gradient of stimulus-dependence: visual cortex dynamics are strongly modulated by stimulus conditions, thalamus shows attenuated modulation, while hippocampus and midbrain maintain relatively invariant dynamics, suggesting distinct computational roles.
  • Methodology Demonstrates that statistical complexity, but not the classical coefficient of variation (CV), can discriminate between different stimulus conditions (natural images, blank screens, spontaneous activity), uncovering structured motifs in population activity.

主要结论

  • Statistical complexity revealed clear, stimulus-specific motifs in population activity across visual cortex, hippocampus, thalamus, and midbrain, while the coefficient of variation (CV) failed to discriminate between natural image presentations, blank screens, and spontaneous activity conditions.
  • Visual cortex subregions exhibited the highest CV values (median range: 0.40–0.59, approximately 2–3× higher than shuffled surrogates, p<0.001), showing strong stimulus-dependent modulation, while midbrain areas displayed the most invariant dynamics across all experimental conditions.
  • The complexity-entropy (C-H) plane framework enabled classification of dynamical regimes, with different brain regions occupying distinct positions: visual cortex showed intermediate entropy with high complexity during stimulus presentation, while surrogate data clustered near the random limit (high entropy, low complexity).
研究空白： While large-scale neural recordings provide rich data, understanding how population dynamics differ across brain regions and behavioral contexts remains challenging. Traditional variability metrics like CV capture temporal fluctuations but fail to reveal the structured, stimulus-dependent organizational patterns in neural activity.

摘要: 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.