Paper List
-
Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
-
Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
-
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...
-
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...
-
The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
-
A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
-
ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
-
DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
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.