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. 2022 Jul 15;5:807406. doi: 10.3389/frai.2022.807406

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Copyright © 2022 Ramezanian-Panahi, Abrevaya, Gagnon-Audet, Voleti, Rish and Dumas.

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(s) 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.

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Venn diagram of the generative models of interest. Based on the abstraction and assumption, methods might belong to one or more of the three worlds of machine learning, neuroscience, and dynamical systems. This review is structured into three main categories that are in fact, intersections of these fields: biophysical (Section 1), phenomenological (Section 2), and agnostic modeling (Section 3). Tools developed independently in each of these fields can be combined to overcome the limitation of data.