Heterogeneous Synaptic Weighting Improves Neural Coding in the Presence of Common Noise

DOI10.1162/neco_a_01287zbMath1450.92006OpenAlexW3028127069WikidataQ95324744 ScholiaQ95324744MaRDI QIDQ5131145

Michael R DeWeese, Pratik S. Sachdeva, Jesse A. Livezey

Publication date: 2 November 2020

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

Full work available at URL: https://escholarship.org/uc/item/4c03j1t8

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20)

Cites Work

• Population Coding with Correlation and an Unfaithful Model
• Representational Accuracy of Stochastic Neural Populations
• Fisher and Shannon Information in Finite Neural Populations
• Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons
• On the Uniqueness and Stability of Dictionaries for Sparse Representation of Noisy Signals
• Mutual Information, Fisher Information, and Efficient Coding
• Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation
• Implications of Neuronal Diversity on Population Coding
• Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix
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