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Advances in Info-MetricsInformation and Information Processing across Disciplines$
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Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah

Print publication date: 2020

Print ISBN-13: 9780190636685

Published to Oxford Scholarship Online: December 2020

DOI: 10.1093/oso/9780190636685.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 17 June 2021

Inferring the Logic of Collective Information Processors

Inferring the Logic of Collective Information Processors

Chapter:
(p.81) 3 Inferring the Logic of Collective Information Processors
Source:
Advances in Info-Metrics
Author(s):

Bryan C. Daniels

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190636685.003.0003

From neurons to insects to societies, across biology we see impressive feats of collective information processing. What strategies do these systems use to perform useful computations? Moving toward an answer to this question, this chapter focuses on common challenges in inferring models of complicated distributed systems and how the perspective of information theory and statistical physics is useful for understanding collective behavior.

Keywords:   collective behavior, collective computation, systems biology, model inference, maximum entropy models, sloppy models, phase transitions, dimensionality reduction, model reduction

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