Thermodynamic cost for precision of general counting observables

We analytically derive universal bounds that describe the trade-off between thermodynamic cost and precision in a sequence of events related to some internal changes of an otherwise hidden physical system. The precision is quantified by the fluctuations in either the number of events counted over time or the times between successive events. Our results are valid for the same broad class of nonequilibrium driven systems considered by the thermodynamic uncertainty relation, but they extend to both time-symmetric and asymmetric observables. We show how optimal precision saturating the bounds can be achieved. For waiting time fluctuations of asymmetric observables, a phase transition in the optimal configuration arises, where higher precision can be achieved by combining several signals.

[1]  M. Baiesi,et al.  Effective estimation of entropy production with lacking data , 2023, 2305.04657.

[2]  Artemy Kolchinsky,et al.  Thermodynamic bound on spectral perturbations , 2023 .

[3]  G. Bisker,et al.  Entropy production rates for different notions of partial information , 2023, Journal of Physics D: Applied Physics.

[4]  S. Krishnamurthy,et al.  Entropy production of resetting processes , 2022, Physical Review Research.

[5]  A. Maritan,et al.  Fluctuations of entropy production of a run-and-tumble particle. , 2022, Physical review. E.

[6]  T. Vu,et al.  Thermodynamic Unification of Optimal Transport: Thermodynamic Uncertainty Relation, Minimum Dissipation, and Thermodynamic Speed Limits , 2022, Physical Review X.

[7]  G. Bisker,et al.  Inferring entropy production rate from partially observed Langevin dynamics under coarse-graining , 2022, Physical chemistry chemical physics : PCCP.

[8]  I. Neri Estimating entropy production rates with first-passage processes , 2022, Journal of Physics A: Mathematical and Theoretical.

[9]  Pedro E. Harunari,et al.  Beat of a current. , 2022, Physical review. E.

[10]  U. Seifert,et al.  Thermodynamic Inference in Partially Accessible Markov Networks: A Unifying Perspective from Transition-Based Waiting Time Distributions , 2022, Physical Review X.

[11]  Van Tuan Vo,et al.  Unified thermodynamic–kinetic uncertainty relation , 2022, Journal of Physics A: Mathematical and Theoretical.

[12]  Pedro E. Harunari,et al.  What to Learn from a Few Visible Transitions’ Statistics? , 2022, Physical Review X.

[13]  C. Tsallis Entropy , 2022, Thermodynamic Weirdness.

[14]  A. C. Barato,et al.  Universal minimal cost of coherent biochemical oscillations. , 2021, Physical review. E.

[15]  David Hartich,et al.  Violation of local detailed balance upon lumping despite a clear timescale separation , 2021, Physical Review Research.

[16]  Jean-Charles Delvenne,et al.  The Thermo-Kinetic Relations , 2021, 2110.13050.

[17]  K. Sekimoto Derivation of the First Passage Time Distribution for Markovian Process on Discrete Network , 2021, 2110.02216.

[18]  Patrick Pietzonka Classical Pendulum Clocks Break the Thermodynamic Uncertainty Relation. , 2021, Physical review letters.

[19]  M. Esposito,et al.  Beyond thermodynamic uncertainty relations: nonlinear response, error-dissipation trade-offs, and speed limits , 2021, 2109.11890.

[20]  S. Dattagupta Stochastic Thermodynamics , 2021, Resonance.

[21]  A. Vulpiani,et al.  Excess and loss of entropy production for different levels of coarse graining. , 2021, Physical review. E.

[22]  J. Ehrich Tightest bound on hidden entropy production from partially observed dynamics , 2021, 2105.08803.

[23]  Dominic J. Skinner,et al.  Estimating Entropy Production from Waiting Time Distributions. , 2021, Physical review letters.

[24]  A. Nambu,et al.  Subthalamic nucleus stabilizes movements by reducing neural spike variability in monkey basal ganglia , 2021, Nature Communications.

[25]  A. Dechant,et al.  Improving Thermodynamic Bounds Using Correlations , 2021, Physical Review X.

[26]  Arnab K. Pal,et al.  Thermodynamic uncertainty relation for first-passage times on Markov chains , 2021, Physical Review Research.

[27]  S. Sasa,et al.  Kinetic uncertainty relation on first-passage time for accumulated current. , 2021, Physical review. E.

[28]  Lubomir Kostal,et al.  Fano Factor: A Potentially Useful Information , 2020, Frontiers in Computational Neuroscience.

[29]  Dominic J. Skinner,et al.  Improved bounds on entropy production in living systems , 2020, Proceedings of the National Academy of Sciences.

[30]  David Hartich,et al.  Emergent Memory and Kinetic Hysteresis in Strongly Driven Networks , 2020, Physical Review X.

[31]  C. Monthus Large deviations for Markov processes with stochastic resetting: analysis via the empirical density and flows or via excursions between resets , 2020, Journal of Statistical Mechanics: Theory and Experiment.

[32]  Van Tuan Vo,et al.  Unified approach to classical speed limit and thermodynamic uncertainty relation. , 2020, Physical review. E.

[33]  A. Stella,et al.  Exact Coarse Graining Preserves Entropy Production out of Equilibrium. , 2020, Physical review letters.

[34]  S. Ganguli,et al.  Universal energy-accuracy tradeoffs in nonequilibrium cellular sensing , 2020, Physical Review E.

[35]  I. Neri Second Law of Thermodynamics at Stopping Times. , 2020, Physical review letters.

[36]  Luis Pedro García-Pintos,et al.  Time–information uncertainty relations in thermodynamics , 2020, Nature Physics.

[37]  Todd R. Gingrich,et al.  Thermodynamic uncertainty relations constrain non-equilibrium fluctuations , 2020, Nature Physics.

[38]  S. Pigolotti,et al.  Hyperaccurate currents in stochastic thermodynamics. , 2019, Physical review. E.

[39]  C. Maes Frenesy: Time-symmetric dynamical activity in nonequilibria , 2019, 1904.10485.

[40]  Édgar Roldán,et al.  Exact distributions of currents and frenesy for Markov bridges. , 2019, Physical review. E.

[41]  U. Seifert From Stochastic Thermodynamics to Thermodynamic Inference , 2019, Annual Review of Condensed Matter Physics.

[42]  R. J. Harris,et al.  Thermodynamic uncertainty for run-and-tumble–type processes , 2019, EPL (Europhysics Letters).

[43]  A. Dechant,et al.  Stochastic Time Evolution, Information Geometry, and the Cramér-Rao Bound , 2018, Physical Review X.

[44]  M. Baiesi,et al.  Kinetic uncertainty relation , 2018, Journal of Physics A: Mathematical and Theoretical.

[45]  Gili Bisker,et al.  Inferring broken detailed balance in the absence of observable currents , 2018, Nature Communications.

[46]  A. Dechant,et al.  Fluctuation–response inequality out of equilibrium , 2018, Proceedings of the National Academy of Sciences.

[47]  S. Krishnamurthy,et al.  Exact results for the finite time thermodynamic uncertainty relation , 2017, 1712.02714.

[48]  Federico S. Gnesotto,et al.  Broken detailed balance and non-equilibrium dynamics in living systems: a review , 2017, Reports on progress in physics. Physical Society.

[49]  A. Dechant,et al.  Current fluctuations and transport efficiency for general Langevin systems , 2017, Journal of Statistical Mechanics: Theory and Experiment.

[50]  Todd R. Gingrich,et al.  Fundamental Bounds on First Passage Time Fluctuations for Currents. , 2017, Physical review letters.

[51]  Massimiliano Esposito,et al.  Effective Thermodynamics for a Marginal Observer. , 2017, Physical review letters.

[52]  F. Ritort,et al.  Finite-time generalization of the thermodynamic uncertainty relation. , 2017, Physical review. E.

[53]  A. C. Barato,et al.  Coherence of biochemical oscillations is bounded by driving force and network topology. , 2017, Physical review. E.

[54]  J. P. Garrahan Simple bounds on fluctuations and uncertainty relations for first-passage times of counting observables. , 2017, Physical review. E.

[55]  Andre C. Barato,et al.  Affinity- and topology-dependent bound on current fluctuations , 2016, 1605.07542.

[56]  Frank Julicher,et al.  Statistics of Infima and Stopping Times of Entropy Production and Applications to Active Molecular Processes , 2016, 1604.04159.

[57]  Todd R. Gingrich,et al.  Dissipation Bounds All Steady-State Current Fluctuations. , 2015, Physical review letters.

[58]  Udo Seifert,et al.  Universal bounds on current fluctuations. , 2015, Physical review. E.

[59]  Yuhai Tu,et al.  The free energy cost of accurate biochemical oscillations , 2015, Nature Physics.

[60]  A. C. Barato,et al.  A Formal View on Level 2.5 Large Deviations and Fluctuation Relations , 2015, Journal of Statistical Physics.

[61]  A. C. Barato,et al.  Universal bound on the Fano factor in enzyme kinetics. , 2015, The journal of physical chemistry. B.

[62]  Udo Seifert,et al.  Thermodynamic uncertainty relation for biomolecular processes. , 2015, Physical review letters.

[63]  A. C. Barato,et al.  A Formal View on Level 2.5 Large Deviations and Fluctuation Relations , 2014, 1408.5033.

[64]  A. Faggionato,et al.  Large deviations of the empirical flow for continuous time Markov chains , 2012, 1210.2004.

[65]  U. Seifert Stochastic thermodynamics, fluctuation theorems and molecular machines , 2012, Reports on progress in physics. Physical Society.

[66]  M. Esposito Stochastic thermodynamics under coarse graining. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[67]  M. Rief,et al.  The Complex Folding Network of Single Calmodulin Molecules , 2011, Science.

[68]  Juan M R Parrondo,et al.  Estimating dissipation from single stationary trajectories. , 2010, Physical review letters.

[69]  U. Seifert,et al.  Communications: Can one identify nonequilibrium in a three-state system by analyzing two-state trajectories? , 2010, The Journal of chemical physics.

[70]  Peter Sollich,et al.  Large deviations and ensembles of trajectories in stochastic models , 2009, 0911.0211.

[71]  C. Maes,et al.  Computation of Current Cumulants for Small Nonequilibrium Systems , 2008, 0807.0145.

[72]  H. Touchette The large deviation approach to statistical mechanics , 2008, 0804.0327.

[73]  C. Maes,et al.  Canonical structure of dynamical fluctuations in mesoscopic nonequilibrium steady states , 2007, 0705.2344.

[74]  R. Elber,et al.  Computing time scales from reaction coordinates by milestoning. , 2004, The Journal of chemical physics.

[75]  Hong Qian,et al.  Fluorescence correlation spectroscopy with high-order and dual-color correlation to probe nonequilibrium steady states. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[76]  R. Rubin,et al.  Fluctuations and randomness of movement of the bead powered by a single kinesin molecule in a force-clamped motility assay: Monte Carlo simulations. , 2002, Biophysical journal.

[77]  Arnaud de La Fortelle,et al.  Large Deviation Principle for Markov Chains in Continuous Time , 2001, Probl. Inf. Transm..

[78]  Z. Koza General technique of calculating the drift velocity and diffusion coefficient in arbitrary periodic systems , 1999, cond-mat/9909204.

[79]  P. Schwille,et al.  Dual-color fluorescence cross-correlation spectroscopy for multicomponent diffusional analysis in solution. , 1997, Biophysical journal.

[80]  Kazuhiko Kinosita,et al.  Direct observation of the rotation of F1-ATPase , 1997, Nature.

[81]  William Bialek,et al.  Bits and brains: Information flow in the nervous system , 1993 .

[82]  William Bialek,et al.  Reading a Neural Code , 1991, NIPS.

[83]  G. Rubino,et al.  Sojourn times in finite Markov processes , 1989, Journal of Applied Probability.

[84]  F. Jülicher,et al.  Quantifying entropy production in active fluctuations of the hair-cell bundle from time irreversibility and uncertainty relations , 2021 .

[85]  AAlemany,et al.  From free energy measurements to thermodynamic inference in nonequilibrium small systems , 2015 .

[86]  R. Lipowsky,et al.  Chemomechanical Coupling of Molecular Motors: Thermodynamics, Network Representations, and Balance Conditions , 2009 .

[87]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[88]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .