Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
Translating Measures onto Mechanisms: The Cognitive Relevance of Higher-Order Information
University of Amsterdam | University of Cambridge | Queen Mary University of London | Imperial College London | University of Vermont | Indiana University | University of Glasgow | Universidad Catolica del Maule | University of Helsinki
30秒速读
IN SHORT: This review addresses the core challenge of translating abstract higher-order information theory metrics (e.g., synergy, redundancy) into defensible, mechanistic explanations for cognitive function in neuroscience.
核心创新
- Methodology Systematizes Shannon-based multivariate metrics (e.g., Total Correlation, Dual Total Correlation, O-information) into a unified framework defined by two independent axes: interaction strength and redundancy-synergy balance.
- Theory Proposes that a balanced layering of synergistic integration and redundant broadcasting optimizes multiscale complexity, formalizing a fundamental computation-communication tradeoff in neural systems.
- Methodology Provides a pragmatic guide for applying Partial Information Decomposition (PID) to neural data, emphasizing the critical conceptual and practical consequences of choosing a specific redundancy function.
主要结论
- Higher-order dependence in multivariate systems can be parsimoniously characterized by two largely independent axes: interaction strength (e.g., quantified by S-information) and redundancy-synergy balance (e.g., quantified by O-information).
- Prototypical systems demonstrate this duality: a purely redundant COPY distribution yields O-information = +1 bit, while a purely synergistic XOR distribution yields O-information = -1 bit, despite both having an S-information of 3 bits.
- The balanced integration of synergistic (head-to-head) and redundant (tail-to-tail) information motifs is proposed as a mechanism optimizing multiscale complexity, formalizing a tradeoff critical for cognitive function.
摘要: Higher–order information theory has become a rapidly growing toolkit in computational neuroscience, motivated by the idea that multivariate dependencies can reveal aspects of neural computation and communication invisible to pairwise analyses. Yet functional interpretations of synergy and redundancy often outpace principled arguments for how statistical quantities map onto mechanistic cognitive processes. Here we review the main families of higher-order measures with the explicit goal of translating mathematical properties into defensible mechanistic inferences. Firstly, we systematize Shannon-based multivariate metrics and demonstrate that higher-order dependence is parsimoniously characterized by two largely independent axes: interaction strength and redundancy-synergy balance. We argue that balanced layering of synergistic integration and redundant broadcasting optimizes multiscale complexity, formalizing a computation-communication tradeoff. We then examine the partial information decomposition and outline pragmatic considerations for its deployment in neural data. Equipped with the relevant mathematical essentials, we connect redundancy-synergy balance to cognitive function by progressively embedding their mathematical properties in real-world constraints, starting with small synthetic systems before gradually building up to neuroimaging. We close by identifying key future directions for mechanistic insight: cross-scale bridging, intervention-based validation, and thermodynamically grounded unification of information dynamics.