Biological computation through recurrence

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  • dc.contributor.author Vidal-Saez, María Sol
  • dc.contributor.author Vilarroya, Óscar
  • dc.contributor.author García Ojalvo, Jordi
  • dc.date.accessioned 2024-09-16T11:53:55Z
  • dc.date.available 2024-09-16T11:53:55Z
  • dc.date.issued 2024
  • dc.description.abstract One of the defining features of living systems is their adaptability to changing environmental conditions. This requires organisms to extract temporal and spatial features of their environment, and use that information to compute the appropriate response. In the last two decades, a growing body of work, mainly coming from the machine learning and computational neuroscience fields, has shown that such complex information processing can be performed by recurrent networks. Temporal computations arise in these networks through the interplay between the external stimuli and the network's internal state. In this article we review our current understanding of how recurrent networks can be used by biological systems, from cells to brains, for complex information processing. Rather than focusing on sophisticated, artificial recurrent architectures such as long short-term memory (LSTM) networks, here we concentrate on simpler network structures and learning algorithms that can be expected to have been found by evolution. We also review studies showing evidence of naturally occurring recurrent networks in living organisms. Lastly, we discuss some relevant evolutionary aspects concerning the emergence of this natural computation paradigm.
  • dc.description.sponsorship This work was supported by project PID2021-127311NB-I00/MICIN/AEI/10.13039/501100 011033/FEDER-UE, financed by the Spanish Ministry of Science and Innovation, the Spanish State Research Agency and the European Regional Development Fund (FEDER). Financial support was also provided by the Maria de Maeztu Programme for Units of Excellence in R&D (Spanish State Research Agency, project CEX2018-000792-M ), by the ICREA Academia programme, and by the Fundacion Tatiana. M.S.V. is supported by a PhD fellowship from the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) from the Generalitat de Catalunya (grant 2021-FI-B-00408).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Vidal-Saez MS, Vilarroya O, Garcia-Ojalvo J. Biological computation through recurrence. Biochem Biophys Res Commun. 2024 Oct 8;728:150301. DOI: 10.1016/j.bbrc.2024.150301
  • dc.identifier.doi http://dx.doi.org/10.1016/j.bbrc.2024.150301
  • dc.identifier.issn 0006-291X
  • dc.identifier.uri http://hdl.handle.net/10230/61099
  • dc.language.iso eng
  • dc.publisher Elsevier
  • dc.relation.ispartof Biochem Biophys Res Commun. 2024 Oct 8;728:150301
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-127311NB-I00
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2018-000792-M
  • dc.rights © 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
  • dc.subject.other Computació evolutiva
  • dc.subject.other Seqüències recurrents (Matemàtica)
  • dc.title Biological computation through recurrence
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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