Paper List
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Translating Measures onto Mechanisms: The Cognitive Relevance of Higher-Order Information
This review addresses the core challenge of translating abstract higher-order information theory metrics (e.g., synergy, redundancy) into defensible, ...
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Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
This paper addresses the critical gap in understanding whether LLMs spontaneously develop human-like Bayesian strategies for processing uncertain info...
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Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
This work addresses the critical lack of experimentally validated channel models for molecular communication within complex vessel networks, which is ...
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Modulation of DNA rheology by a transcription factor that forms aging microgels
This work addresses the fundamental question of how the transcription factor NANOG, essential for embryonic stem cell pluripotency, physically regulat...
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Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks
This paper addresses the core challenge of distinguishing genuine stochastic dynamics of gene regulatory networks from artifacts introduced by imperfe...
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PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
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Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
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Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
Revealing stimulus-dependent dynamics through statistical complexity
Universidade Federal de Pernambuco | University of Minho | University of Arkansas | Universidade Federal de Alagoas
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).
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.