TY - JOUR
T1 - Decomposing the Local Arrow of Time in Interacting Systems
AU - Lynn, Christopher W.
AU - Holmes, Caroline M.
AU - Bialek, William
AU - Schwab, David J.
N1 - Funding Information:
We thank S. E. Palmer for helpful discussions and for guiding us through the data of Ref. . This work was supported in part by the National Science Foundation, through the Center for the Physics of Biological Function (PHY–1734030) and a Graduate Research Fellowship (C. M. H.); by the National Institutes of Health through the BRAIN initiative (R01EB026943); by the James S. McDonnell Foundation through a Postdoctoral Fellowship Award (C. W. L.); by the Simons Foundation; and by a Sloan Research Fellowship (D. J. S.).
Publisher Copyright:
© 2022 American Physical Society.
PY - 2022/9/9
Y1 - 2022/9/9
N2 - We show that the evidence for a local arrow of time, which is equivalent to the entropy production in thermodynamic systems, can be decomposed. In a system with many degrees of freedom, there is a term that arises from the irreversible dynamics of the individual variables, and then a series of non-negative terms contributed by correlations among pairs, triplets, and higher-order combinations of variables. We illustrate this decomposition on simple models of noisy logical computations, and then apply it to the analysis of patterns of neural activity in the retina as it responds to complex dynamic visual scenes. We find that neural activity breaks detailed balance even when the visual inputs do not, and that this irreversibility arises primarily from interactions between pairs of neurons.
AB - We show that the evidence for a local arrow of time, which is equivalent to the entropy production in thermodynamic systems, can be decomposed. In a system with many degrees of freedom, there is a term that arises from the irreversible dynamics of the individual variables, and then a series of non-negative terms contributed by correlations among pairs, triplets, and higher-order combinations of variables. We illustrate this decomposition on simple models of noisy logical computations, and then apply it to the analysis of patterns of neural activity in the retina as it responds to complex dynamic visual scenes. We find that neural activity breaks detailed balance even when the visual inputs do not, and that this irreversibility arises primarily from interactions between pairs of neurons.
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U2 - 10.1103/PhysRevLett.129.118101
DO - 10.1103/PhysRevLett.129.118101
M3 - Article
C2 - 36154397
AN - SCOPUS:85138299814
SN - 0031-9007
VL - 129
JO - Physical review letters
JF - Physical review letters
IS - 11
M1 - 118101
ER -