Optimal prediction with resource constraints using the information bottleneck.

Responding to stimuli requires that organisms encode information about the external world. Not all parts of the input are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the...

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Autores principales:	Vedant Sachdeva, Thierry Mora, Aleksandra M Walczak, Stephanie E Palmer
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9ce5c96ae8b149df85fe2bee5a51bcb4
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id	oai:doaj.org-article:9ce5c96ae8b149df85fe2bee5a51bcb4
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spelling	oai:doaj.org-article:9ce5c96ae8b149df85fe2bee5a51bcb42021-12-02T19:57:35ZOptimal prediction with resource constraints using the information bottleneck.1553-734X1553-735810.1371/journal.pcbi.1008743https://doaj.org/article/9ce5c96ae8b149df85fe2bee5a51bcb42021-03-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008743https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Responding to stimuli requires that organisms encode information about the external world. Not all parts of the input are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future using the information bottleneck approach, for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position information is more useful for accurate prediction. We show which motion representations are easiest to re-use for accurate prediction in other motion contexts, and identify and quantify those with the highest transferability. For non-Markovian dynamics, we explore the role of long-term memory in shaping the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories.Vedant SachdevaThierry MoraAleksandra M WalczakStephanie E PalmerPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 3, p e1008743 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Vedant Sachdeva Thierry Mora Aleksandra M Walczak Stephanie E Palmer Optimal prediction with resource constraints using the information bottleneck.
description	Responding to stimuli requires that organisms encode information about the external world. Not all parts of the input are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future using the information bottleneck approach, for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position information is more useful for accurate prediction. We show which motion representations are easiest to re-use for accurate prediction in other motion contexts, and identify and quantify those with the highest transferability. For non-Markovian dynamics, we explore the role of long-term memory in shaping the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories.
format	article
author	Vedant Sachdeva Thierry Mora Aleksandra M Walczak Stephanie E Palmer
author_facet	Vedant Sachdeva Thierry Mora Aleksandra M Walczak Stephanie E Palmer
author_sort	Vedant Sachdeva
title	Optimal prediction with resource constraints using the information bottleneck.
title_short	Optimal prediction with resource constraints using the information bottleneck.
title_full	Optimal prediction with resource constraints using the information bottleneck.
title_fullStr	Optimal prediction with resource constraints using the information bottleneck.
title_full_unstemmed	Optimal prediction with resource constraints using the information bottleneck.
title_sort	optimal prediction with resource constraints using the information bottleneck.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9ce5c96ae8b149df85fe2bee5a51bcb4
work_keys_str_mv	AT vedantsachdeva optimalpredictionwithresourceconstraintsusingtheinformationbottleneck AT thierrymora optimalpredictionwithresourceconstraintsusingtheinformationbottleneck AT aleksandramwalczak optimalpredictionwithresourceconstraintsusingtheinformationbottleneck AT stephanieepalmer optimalpredictionwithresourceconstraintsusingtheinformationbottleneck
_version_	1718375792074817536

Optimal prediction with resource constraints using the information bottleneck.

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