Entorhinal mismatch: a model of self-supervised learning in the hippocampus
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- dc.contributor.author Santos-Pata, Diogo
- dc.contributor.author Amil, Adrián F.
- dc.contributor.author Raikov, Ivan Georgiev
- dc.contributor.author Rennó-Costa, César
- dc.contributor.author Mura, Anna
- dc.contributor.author Soltesz, Ivan
- dc.contributor.author Verschure, Paul F. M. J.
- dc.date.accessioned 2023-03-03T07:49:03Z
- dc.date.available 2023-03-03T07:49:03Z
- dc.date.issued 2021
- dc.description.abstract The hippocampal formation displays a wide range of physiological responses to different spatial manipulations of the environment. However, very few attempts have been made to identify core computational principles underlying those hippocampal responses. Here, we capitalize on the observation that the entorhinal-hippocampal complex (EHC) forms a closed loop and projects inhibitory signals “countercurrent” to the trisynaptic pathway to build a self-supervised model that learns to reconstruct its own inputs by error backpropagation. The EHC is then abstracted as an autoencoder, with the hidden layers acting as an information bottleneck. With the inputs mimicking the firing activity of lateral and medial entorhinal cells, our model is shown to generate place cells and to respond to environmental manipulations as observed in rodent experiments. Altogether, we propose that the hippocampus builds conjunctive compressed representations of the environment by learning to reconstruct its own entorhinal inputs via gradient descent.
- dc.description.sponsorship This research was supported by the EC grants Virtual Brain Cloud (number 826421) and iNavigate (number 873178), and by NPAD/UFRN. The contributions by I.S. and I.G.R. were supported by an NIH BRAIN Initiative grant (U19 NS104590).
- dc.format.mimetype application/pdf
- dc.identifier.citation Santos-Pata D, Amil AF, Raikov IG, Rennó-Costa C, Mura A, Soltesz I, Verschure PFMJ. Entorhinal mismatch: a model of self-supervised learning in the hippocampus. iScience. 2021;24(4):102364. DOI: 10.1016/j.isci.2021.102364
- dc.identifier.doi http://dx.doi.org/10.1016/j.isci.2021.102364
- dc.identifier.issn 2589-0042
- dc.identifier.uri http://hdl.handle.net/10230/56025
- dc.language.iso eng
- dc.publisher Elsevier
- dc.relation.ispartof iScience. 2021;24(4):102364.
- dc.relation.isreferencedby https://gitlab.com/diogo.santos.pata/encore
- dc.relation.isreferencedby https://ars.els-cdn.com/content/image/1-s2.0-S2589004221003321-mmc1.pdf
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/826421
- dc.rights © 2021. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
- dc.subject.keyword Cognitive Neuroscience
- dc.subject.keyword Neural Networks
- dc.subject.keyword Systems Neuroscience
- dc.title Entorhinal mismatch: a model of self-supervised learning in the hippocampus
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion