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      Information and Efficiency in the Nervous System—A Synthesis

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          Abstract

          In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components—like genetic circuits, biochemical cascades, and ion channels, among others—enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode—with almost 20–60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.

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          Most cited references50

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          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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            Some informational aspects of visual perception.

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              A free energy principle for the brain.

              By formulating Helmholtz's ideas about perception, in terms of modern-day theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts: using constructs from statistical physics, the problems of inferring the causes of sensory input and learning the causal structure of their generation can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organisation and responses. In this paper, we show these perceptual processes are just one aspect of emergent behaviours of systems that conform to a free energy principle. The free energy considered here measures the difference between the probability distribution of environmental quantities that act on the system and an arbitrary distribution encoded by its configuration. The system can minimise free energy by changing its configuration to affect the way it samples the environment or change the distribution it encodes. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment assumes that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at the models entailed by the brain and how minimisation of its free energy can explain its dynamics and structure.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                July 2013
                July 2013
                25 July 2013
                : 9
                : 7
                : e1003157
                Affiliations
                [1 ]The Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
                [2 ]Centre for Neuroscience, Indian Institute of Science, Bangalore, India
                [3 ]Bernstein Centre Munich, Institute of Neurobiology, Ludwig Maximilians Universität, München, Germany
                Indiana University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-13-00190
                10.1371/journal.pcbi.1003157
                3723496
                23935475
                7f9c6c1b-ad62-4768-8f24-fad3a6cc5586
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 February 2013
                : 7 June 2013
                Page count
                Pages: 12
                Funding
                This work is supported by a Wellcome Trust/DBT Early Career fellowship to BS. BS is also grateful to financial support obtained from the ESF, Boehringer Ingelheim Fonds, and the EMBO. MBS is supported via funding from the BMBF. KJF is supported by the Wellcome Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Review
                Biology
                Biophysics
                Computational Biology
                Computational Neuroscience
                Theoretical Biology
                Physics
                Biophysics
                Interdisciplinary Physics
                Social and Behavioral Sciences
                Information Science
                Information Theory

                Quantitative & Systems biology
                Quantitative & Systems biology

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