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. 2021 Nov 2;11:21429. doi: 10.1038/s41598-021-00864-9

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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(A) A visualization of the hippocampal model used for computational simulations. The input layer, EC_in, represents superficial layers of entorhinal cortex. Information flows along two paths, the monosynaptic and trisynaptic pathways (MSP and TSP). Hidden layers represent hippocampal subfields dentate gyrus (DG), cornu ammonis 1 (CA1) and cornu ammonis 3 (CA3). The model acts as a simple encoder and attempts to replicate its input in EC_out, which represents deep layers of entorhinal cortex. EC_in also receives input from EC_out to simulate big-loop recurrence. (B) Vector notation for prototypes, rule-followers, and exceptions (P, R, and E, respectively) in categories A and B. Dimensions 1 through 4 represent the features outer petal colour, outer petal shape, inner petal shape, and central disc colour; dimension 5 indicates category. All features are binary and represented in a padded format in the model (i.e., ‘10’ or ‘01’).