Information-Theoretic Bounds and Approximations in Neural Population Coding

DOI10.1162/neco_a_01056zbMath1472.92022arXiv1611.01414OpenAlexW2551959291WikidataQ50000079 ScholiaQ50000079MaRDI QIDQ5157152

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Publication date: 12 October 2021

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

Full work available at URL: https://arxiv.org/abs/1611.01414

zbMATH Keywords

neural population codingShannon's mutual informationinformation-theoretic asymptotic bounds

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Coding theorems (Shannon theory) (94A24)

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Cites Work

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• A Mathematical Theory of Communication
• Fisher and Shannon Information in Finite Neural Populations
• Information-theoretic asymptotics of Bayes methods
• Mutual Information and Minimum Mean-Square Error in Gaussian Channels
• Optimal Short-Term Population Coding: When Fisher Information Fails
• Estimation of Entropy and Mutual Information
• Fisher information and stochastic complexity
• Efficient Sensory Encoding and Bayesian Inference with Heterogeneous Neural Populations
• Mutual Information, Fisher Information, and Efficient Coding
• Elements of Information Theory
• Difficulty of Singularity in Population Coding

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