abstract bibtex

A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system's state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive. All systems perform computations by means of re-sponding to their environment. In particular, living sys-tems compute, on a variety of length-and time-scales, fu-ture expectations based on their prior experience. Most biological computation is fundamentally a nonequilib-rium process, because a preponderance of biological ma-chinery in its natural operation is driven far from thermo-dynamic equilibrium. For example, many molecular ma-chines (such as the microtubule-associated motor kinesin) are driven by ATP hydrolysis, which liberates ∼500 meV per molecule [1]. This energy is large compared with ambient thermal energy, 1 k B T ≈ 25 meV (k B is Boltz-mann's constant and the temperature is T ∼ 300 Kelvin). In general, such large energetic inputs drive the opera-tive degrees of freedom of biological machines away from equilibrium averages.

@article{Still, abstract = {A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system's state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive. All systems perform computations by means of re-sponding to their environment. In particular, living sys-tems compute, on a variety of length-and time-scales, fu-ture expectations based on their prior experience. Most biological computation is fundamentally a nonequilib-rium process, because a preponderance of biological ma-chinery in its natural operation is driven far from thermo-dynamic equilibrium. For example, many molecular ma-chines (such as the microtubule-associated motor kinesin) are driven by ATP hydrolysis, which liberates ∼500 meV per molecule [1]. This energy is large compared with ambient thermal energy, 1 k B T ≈ 25 meV (k B is Boltz-mann's constant and the temperature is T ∼ 300 Kelvin). In general, such large energetic inputs drive the opera-tive degrees of freedom of biological machines away from equilibrium averages.}, author = {Still, Susanne and Sivak, David A and Bell, Anthony J and Crooks, Gavin E}, file = {:Users/brekels/Documents/Mendeley Desktop/The thermodynamics of prediction - Still et al.pdf:pdf}, title = {{The thermodynamics of prediction}} }

Downloads: 0