Information-Geometric Measures as Robust Estimators of Connection Strengths and External Inputs

DOI10.1162/neco.2009.04-08-748zbMath1167.92007OpenAlexW2099681619WikidataQ51819291 ScholiaQ51819291MaRDI QIDQ5323765

Masami Tatsuno, Jean-Marc Fellous, Shun-ichi Amari

Publication date: 30 July 2009

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2009.04-08-748

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Neural biology (92C20) Statistical aspects of information-theoretic topics (62B10)

Related Items (6)

Estimation of neural connections from partially observed neural spikes ⋮ Intrinsic Graph Structure Estimation Using Graph Laplacian ⋮ Conditional Mixture Model for Correlated Neuronal Spikes ⋮ Influence of External Inputs and Asymmetry of Connections on Information-Geometric Measures Involving Up to Ten Neuronal Interactions ⋮ Correlation-driven framework based on graph convolutional network for clinical disease classification ⋮ Information-Geometric Measures for Estimation of Connection Weight Under Correlated Inputs

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• A Comparison of Descriptive Models of a Single Spike Train by Information-Geometric Measure

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