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 changes with slight...
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| Format: | Article |
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MDPI AG
2018
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| Online Access: | http://hdl.handle.net/1721.1/117535 |
| _version_ | 1811096594692440064 |
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| author | Marzen, Sarah E. |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Marzen, Sarah E. |
| author_sort | Marzen, Sarah E. |
| collection | MIT |
| 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. Keywords: statistical complexity; intrinsic computation; excess entropy |
| first_indexed | 2024-09-23T16:45:51Z |
| format | Article |
| id | mit-1721.1/117535 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:45:51Z |
| publishDate | 2018 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | mit-1721.1/1175352022-10-03T08:10:33Z Intrinsic Computation of a Monod-Wyman-Changeux Molecule Marzen, Sarah E. Massachusetts Institute of Technology. Department of Physics Marzen, Sarah E. 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. Keywords: statistical complexity; intrinsic computation; excess entropy 2018-08-27T14:43:53Z 2018-08-27T14:43:53Z 2018-08 2018-08 2018-08-22T08:32:11Z Article http://purl.org/eprint/type/JournalArticle 1099-4300 http://hdl.handle.net/1721.1/117535 Marzen, Sarah. "Intrinsic Computation of a Monod-Wyman-Changeux Molecule." Entropy 20, 8 (August 2018): 599 © 2018 The Authors http://dx.doi.org/10.3390/e20080599 Entropy Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf MDPI AG Multidisciplinary Digital Publishing Institute |
| spellingShingle | Marzen, Sarah E. Intrinsic Computation of a Monod-Wyman-Changeux Molecule |
| 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 |
| url | http://hdl.handle.net/1721.1/117535 |
| work_keys_str_mv | AT marzensarahe intrinsiccomputationofamonodwymanchangeuxmolecule |