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Intrinsic Computation of a Monod-Wyman-Changeux Molecule

Physics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Entropy 2018, 20(8), 599;
Received: 12 July 2018 / Revised: 6 August 2018 / Accepted: 10 August 2018 / Published: 11 August 2018
(This article belongs to the Special Issue Information Theory in Complex Systems)
Causal states are minimal sufficient statistics of prediction of a stochastic process, their coding cost is called statistical complexity, and the implied causal structure yields a sense of the process’ “intrinsic computation”. We discuss how statistical complexity changes with slight changes to the underlying model– in this case, a biologically-motivated dynamical model, that of a Monod-Wyman-Changeux molecule. Perturbations to kinetic rates cause statistical complexity to jump from finite to infinite. The same is not true for excess entropy, the mutual information between past and future, or for the molecule’s transfer function. We discuss the implications of this for the relationship between intrinsic and functional computation of biological sensory systems. View Full-Text
Keywords: statistical complexity; intrinsic computation; excess entropy statistical complexity; intrinsic computation; excess entropy
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MDPI and ACS Style

Marzen, S. Intrinsic Computation of a Monod-Wyman-Changeux Molecule. Entropy 2018, 20, 599.

AMA Style

Marzen S. Intrinsic Computation of a Monod-Wyman-Changeux Molecule. Entropy. 2018; 20(8):599.

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Marzen, Sarah. 2018. "Intrinsic Computation of a Monod-Wyman-Changeux Molecule" Entropy 20, no. 8: 599.

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