Optimizing chemotaxis by measuring unbound-bound transitions

DOI10.1016/j.physd.2009.09.009zbMath1186.92011OpenAlexW2039577828MaRDI QIDQ964877

Peter Dayan, Duncan Mortimer, Kevin Burrage, Geoffrey J. Goodhill

Publication date: 21 April 2010

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physd.2009.09.009

zbMATH Keywords

Bayesian modelaxon guidancegrowth cone

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Neural biology (92C20) Biomedical imaging and signal processing (92C55) Cell movement (chemotaxis, etc.) (92C17)

Related Items (4)

Know the single-receptor sensing limit? Think again ⋮ Optimality and Saturation in Axonal Chemotaxis ⋮ The influence of receptor positioning on chemotactic information ⋮ Bayes-Optimal Chemotaxis

Cites Work

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• Stochastic model of leukocyte chemosensory movement
• A mathematical framework for modeling axon guidance
• Predicting Axonal Response to Molecular Gradients with a Computational Model of Filopodial Dynamics

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