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Using a simple neural network to delineate some principles of distributed economic choice

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dc.contributor.author Balasubramani, Pragathi P.
dc.contributor.author Moreno Bote, Rubén
dc.contributor.author Hayden, Benjamin Y.
dc.date.accessioned 2019-06-18T08:48:56Z
dc.date.available 2019-06-18T08:48:56Z
dc.date.issued 2018
dc.identifier.citation Balasubramani PP, Moreno Bote R, Hayden BY. Using a simple neural network to delineate some principles of distributed economic choice. Front Comput Neurosci. 2018 Mar 28;12:22. DOI: 10.3389/fncom.2018.00022
dc.identifier.issn 1662-5188
dc.identifier.uri http://hdl.handle.net/10230/41810
dc.description.abstract The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.
dc.description.sponsorship This work was supported by an R01 from NIH to BH (DA037229) and grants PSI2013-44811-P and FLAGERA-PCIN-2015-162-C02-02 from MINECO (Spain) to RM-B.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers
dc.relation.ispartof Frontiers in Computational Neuroscience. 2018 Mar 28;12:22.
dc.rights © 2018 Balasubramani, Moreno-Bote and Hayden. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Using a simple neural network to delineate some principles of distributed economic choice
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://dx.doi.org/10.3389/fncom.2018.00022
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PSI2013-44811-P
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PCIN-2015-162-C02-02
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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