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Optimal inference strategies and their implications for the linear noise approximation
1II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany.
Physical Review. E
|November 15, 2016
Summary
Information loss in binary sensors using time averaging can reach 0.5 bits per measurement. This loss is avoided if signal-induced free energy is symmetrically distributed in forward and backward rates, ensuring full signal capture.
Area of Science:
- Statistical physics
- Information theory
- Biophysics
Background:
- Many biological and physical systems rely on sensors to infer environmental signals.
- Time-averaging strategies are common inference methods but can lead to information loss.
- Understanding information loss is crucial for designing efficient sensing mechanisms.
Purpose of the Study:
- To quantify information loss in time-averaging inference strategies for binary sensors.
- To identify conditions that prevent information loss in such systems.
- To explore the implications of information loss in the linear noise approximation.
Main Methods:
- Analysis of information loss in arrays of independent binary sensors.
- Derivation of a condition based on the local detailed balance relation.
- Investigation of free-energy distribution among forward and backward rates.
- Application of the linear noise approximation.
Main Results:
- Information loss can be as high as 0.5 bits per measurement, independent of sensor number.
- A condition related to symmetric free-energy distribution prevents information loss.
- Time integration mechanisms capture full information when this condition is met.
- The linear noise approximation exhibits similar information loss due to its inherent simplifications.
Conclusions:
- Time-averaging inference strategies can lead to significant, quantifiable information loss in binary sensor arrays.
- The local detailed balance relation and symmetric free-energy distribution are key to preserving information.
- These findings have implications for designing efficient sensors in biophysics and statistical physics.
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