Bayes-optimal chemotaxis

Mortimer, Duncan, Dayan, Peter, Burrage, Kevin and Goodhill, Geoffrey J. (2011) Bayes-optimal chemotaxis. Neural Computation, 23 2: 336-373. doi:10.1162/NECO_a_00075

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Author Mortimer, Duncan
Dayan, Peter
Burrage, Kevin
Goodhill, Geoffrey J.
Title Bayes-optimal chemotaxis
Journal name Neural Computation   Check publisher's open access policy
ISSN 0899-7667
Publication date 2011-02-01
Year available 2011
Sub-type Article (original research)
DOI 10.1162/NECO_a_00075
Open Access Status File (Publisher version)
Volume 23
Issue 2
Start page 336
End page 373
Total pages 38
Place of publication Cambridge, MA, United States
Publisher M I T Press
Language eng
Formatted abstract
Chemotaxis plays a crucial role in many biological processes, including nervous system development. However, fundamental physical constraints limit the ability of a small sensing device such as a cell or growth cone to detect an external chemical gradient. One of these is the stochastic nature of receptor binding, leading to a constantly fluctuating binding pattern across the cell's array of receptors. This is analogous to the uncertainty in sensory information often encountered by the brain at the systems level. Here we derive analytically the Bayes-optimal strategy for combining information from a spatial array of receptors in both one and two dimensions to determine gradient direction. We also show how information from more than one receptor species can be optimally integrated, derive the gradient shapes that are optimal for guiding cells or growth cones over the longest possible distances, and illustrate that polarized cell behavior might arise as an adaptation to slowly varying environments. Together our results provide closed-form predictions for variations in chemotactic performance over a wide range of gradient conditions.
Keyword Computer Science, Artificial Intelligence
Computer Science
Neurosciences & Neurology
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID DP0666126
Institutional Status UQ
Additional Notes Posted Online 11 January, 2011.

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Citation counts: TR Web of Science Citation Count  Cited 11 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 12 times in Scopus Article | Citations
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Created: Thu, 10 Feb 2011, 21:41:07 EST by Kay Mackie on behalf of Queensland Brain Institute