Embedded Polygon Symbolic Transfer Entropy (EPSTE): A Geometric Token and Deep Learning Approach to Estimating Transfer Entropy in Neuroimaging Time Series

• URL: http://arxiv.org/abs/2606.21754v1
• Date: Fri, 19 Jun 2026 21:13:56 GMT
• Title: Embedded Polygon Symbolic Transfer Entropy (EPSTE): A Geometric Token and Deep Learning Approach to Estimating Transfer Entropy in Neuroimaging Time Series
• Abstract summary: This work introduces Embedded Polygon Transfer Entropy (EPSTE), a framework that reframes TE estimation as a learnable problem.<n>A recurrent neural network with attention-based multiple-instance learning is trained to predict surrogate-validated TE values from bags of symbolic temporal windows.<n>The results demonstrate that while local window-level predictions are noisy, aggregation across trials and channel pairs yields stable directed dependencies.
• Score: 2.1575136240463597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inferring directed interactions between neural systems from EEG and MEG remains challenging due to noise, nonstationarity, and the high sample complexity of information-theoretic estimators. Transfer Entropy (TE) provides a principled and model-free measure of directed information flow; however, its practical estimation is not stable in finite data regimes (particularly as embedding dimension increases). This work introduces Embedded Polygon Symbolic Transfer Entropy (EPSTE), a framework that reframes TE estimation as a learnable problem operating on structured symbolic representations of local temporal morphology rather than raw signal amplitudes. Neural time series are decomposed into sequences of geometric primitives derived from local triplets of samples encoding complementary aspects of waveform structure such as magnitude, curvature and directional change. These primitives are discretised into symbolic tokens, yielding a compact but expressive state space over which symbolic TE is estimated. A recurrent neural network with attention-based multiple-instance learning is trained to predict surrogate-validated TE values from bags of symbolic temporal windows. The method is evaluated on source-reconstructed MEG data parcellated using the AAL90 atlas and compared against a standard symbolic baseline using identical architectures and supervision. The results demonstrate that while local window-level predictions are noisy, aggregation across trials and channel pairs yields stable directed dependencies. At the pair level, EPSTE achieves near-perfect recovery of ground-truth directed structure and significantly lower absolute error than the baseline, indicating that representational geometry plays a critical role in enabling practical learnability of information-theoretic dependencies.

Related papers

• Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations [12.299177496689621]
  We propose a semi-supervised framework combining conditional intrinsic with low-dimensional latent representations.<n>RepG decomposes generation into two stages: label-dependent latent sampling and high-dimensional reconstruction.
  arXiv  Detail & Related papers  (2026-07-18T09:21:53Z)
• Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations [58.079925240809644]
  We propose an end-to-end graph neural surrogate tailored to CO$$ibility plume migration forecasting in geological storage.<n>The method is evaluated on the SPE11A benchmark, a well-known industry test case designed to assess CO$$ storage scenarios.<n>The proposed model produces competitive forecasts of gas saturation and liquid-phase density, with cumulative errors that remain moderate over extended forecasting horizons.
  arXiv  Detail & Related papers  (2026-06-15T18:19:17Z)
• Topological Flow Matching [56.16372813551555]
  Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance.<n>We introduce topological flow matching, a topology-aware generalization of flow matching.<n>We demonstrate its effectiveness on diverse structured datasets, including brain fMRIs, ocean currents, seismic events, and traffic flows.
  arXiv  Detail & Related papers  (2026-06-14T16:29:33Z)
• Monitoring Neural Training with Topology: A Footprint-Predictable Collapse Index [51.56484100374058]
  We propose an online, topology-aware monitor for evolving neural representations that couples Modular Morse Homology Maintenance (MMHM) with a composite Collapse Index (CI)<n>Instead of rebuilding complexes each epoch, we apply sparse edits at a fixed scale and maintain a discrete Morse matching, yielding fast, incremental updates.
  arXiv  Detail & Related papers  (2026-04-28T19:21:21Z)
• Geometrically Constrained Outlier Synthesis [4.254099382808598]
  Deep neural networks for image classification often exhibit overconfidence on out-of-distribution (OOD) samples.<n>We introduce Geometrically Constrained Outlier Synthesis (GCOS), a training-time regularization framework.<n>The framework naturally transitions to conformal OOD inference, which translates uncertainty scores into statistically valid p-values.
  arXiv  Detail & Related papers  (2026-03-09T14:11:47Z)
• Bayesian Matrix Completion Under Geometric Constraints [3.5522446024799064]
  The completion of a Euclidean distance matrix from sparse and noisy observations is a fundamental challenge in signal processing.<n>Traditional approaches, such as rank-constrained optimization and semidefinite programming, enforce geometric constraints but often struggle under sparse or noisy conditions.<n>This paper introduces a hierarchical Bayesian framework that places structured priors directly on the latent point set generating the EDM, naturally embedding geometric constraints.
  arXiv  Detail & Related papers  (2026-01-30T09:45:34Z)
• Time Series Analysis of Spiking Neural Systems via Transfer Entropy and Directed Persistent Homology [0.0]
  We present a framework for analysing neural time series that integrates Transfer Entropy (TE) with Persistent Homology (PH)<n>TE quantifies directional influence between neurons, producing weighted, directed graphs that reflect dynamic interactions.<n>We apply this framework to synthetic spiking networks trained on logic gate tasks, image-classification networks exposed to structured and perturbed inputs, and mouse cortical recordings annotated with behavioral events.
  arXiv  Detail & Related papers  (2025-08-26T14:01:53Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Communities in the Kuramoto Model: Dynamics and Detection via Path Signatures [1.024113475677323]
  We propose the use of path signatures, a mathematical framework that encodes geometric and temporal properties of continuous paths.<n>We propose a novel signature-based community detection algorithm, achieving exact recovery of structural communities from observed time series.<n>Our results demonstrate that path signatures provide a novel perspective on analyzing complex neural data and other high-dimensional systems.
  arXiv  Detail & Related papers  (2025-03-21T21:41:48Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.