Intrinsic Computation of a Monod-Wyman-Changeux Molecule
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 chan...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/8/599 |
| _version_ | 1828357780535246848 |
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| author | Sarah Marzen |
| author_facet | Sarah Marzen |
| author_sort | Sarah Marzen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T03:19:48Z |
| format | Article |
| id | doaj.art-2b6d9119326c4fe1b2d2004a29921d9e |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T03:19:48Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-2b6d9119326c4fe1b2d2004a29921d9e2022-12-22T02:15:20ZengMDPI AGEntropy1099-43002018-08-0120859910.3390/e20080599e20080599Intrinsic Computation of a Monod-Wyman-Changeux MoleculeSarah Marzen0Physics of Living Systems Group, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USACausal 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.http://www.mdpi.com/1099-4300/20/8/599statistical complexityintrinsic computationexcess entropy |
| spellingShingle | Sarah Marzen Intrinsic Computation of a Monod-Wyman-Changeux Molecule Entropy statistical complexity intrinsic computation excess entropy |
| title | Intrinsic Computation of a Monod-Wyman-Changeux Molecule |
| title_full | Intrinsic Computation of a Monod-Wyman-Changeux Molecule |
| title_fullStr | Intrinsic Computation of a Monod-Wyman-Changeux Molecule |
| title_full_unstemmed | Intrinsic Computation of a Monod-Wyman-Changeux Molecule |
| title_short | Intrinsic Computation of a Monod-Wyman-Changeux Molecule |
| title_sort | intrinsic computation of a monod wyman changeux molecule |
| topic | statistical complexity intrinsic computation excess entropy |
| url | http://www.mdpi.com/1099-4300/20/8/599 |
| work_keys_str_mv | AT sarahmarzen intrinsiccomputationofamonodwymanchangeuxmolecule |