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論文資訊
- 類型：已發表論文
- 日期：2020-01-28
摘要
生物 sensors must often predict their input while operating under metabolic constraints. However, determining whether or not a particular sensor is evolved or designed to be accurate and efficient is challenging. This arises partly from the functional constraints being at cross purposes and partly since quantifying the prediction performance of even in silico sensors can require prohibitively long simulations, especially when highly complex environments drive sensors out of equilibrium. To circumvent these difficulties, we develop new expressions for the prediction accuracy and 熱力學 costs of the broad class of conditionally 馬可夫ian sensors subject to complex, correlated (unifilar hidden semi-馬可夫) environmental inputs in nonequilibrium steady state. Predictive metrics include the instantaneous
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