Condensed Matter > Statistical Mechanics
[Submitted on 12 May 2016 (v1), last revised 19 Oct 2016 (this version, v2)]
Title:Optimal inference strategies and their implications for the linear noise approximation
View PDFAbstract:We study the information loss of a class of inference strategies that is solely based on time averaging. For an array of independent binary sensors (e.g., receptors, single electron transistors) measuring a weak random signal (e.g., ligand concentration, gate voltage) this information loss is up to 0.5 bit per measurement irrespective of the number of sensors. We derive a condition related to the local detailed balance relation that determines whether or not such a loss of information occurs. Specifically, if the free energy difference arising from the signal is symmetrically distributed among the forward and backward rates, time integration mechanisms will capture the full information about the signal. As an implication, for the linear noise approximation, we can identify the same loss of information, arising from its inherent simplification of the dynamics.
Submission history
From: David Hartich [view email][v1] Thu, 12 May 2016 18:08:42 UTC (219 KB)
[v2] Wed, 19 Oct 2016 14:27:46 UTC (423 KB)
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