Paper List
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Developing the PsyCogMetrics™ AI Lab to Evaluate Large Language Models and Advance Cognitive Science
This paper addresses the critical gap between sophisticated LLM evaluation needs and the lack of accessible, scientifically rigorous platforms that in...
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Equivalence of approximation by networks of single- and multi-spike neurons
This paper resolves the fundamental question of whether single-spike spiking neural networks (SNNs) are inherently less expressive than multi-spike SN...
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The neuroscience of transformers
提出了Transformer架构与皮层柱微环路之间的新颖计算映射，连接了现代AI与神经科学。
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Framing local structural identifiability and observability in terms of parameter-state symmetries
This paper addresses the core challenge of systematically determining which parameters and states in a mechanistic ODE model can be uniquely inferred ...
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Leveraging Phytolith Research using Artificial Intelligence
This paper addresses the critical bottleneck in phytolith research by automating the labor-intensive manual microscopy process through a multimodal AI...
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Neural network-based encoding in free-viewing fMRI with gaze-aware models
This paper addresses the core challenge of building computationally efficient and ecologically valid brain encoding models for naturalistic vision by ...
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Scalable DNA Ternary Full Adder Enabled by a Competitive Blocking Circuit
This paper addresses the core bottleneck of carry information attenuation and limited computational scale in DNA binary adders by introducing a scalab...
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ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics
This paper addresses the critical bottleneck of translating high-dimensional single-cell transcriptomic data into interpretable biological hypotheses ...
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.