Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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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 ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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
The 30-Second View
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.
Innovation (TL;DR)
- 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.
Key conclusions
- 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.
Abstract: 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.