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To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/ The first 200 of you will get 20% off Brilliant’s annual premium subscription. My name is Artem, I'm a computational neuroscience student and researcher. In this video we discuss the Tolman-Eichenbaum Machine – a computational model of a hippocampal formation, which unifies memory and spatial navigation under a common framework. Patreon: https://www.patreon.com/artemkirsanov Twitter: https://twitter.com/ArtemKRSV OUTLINE: 00:00 - Introduction 01:13 - Motivation: Agents, Rewards and Actions 03:17 - Prediction Problem 05:58 - Model architecture 06:46 - Position module 07:40 - Memory module 08:57 - Running TEM step-by-step 11:37 - Model performance 13:33 - Cellular representations 17:48 - TEM predicts remapping laws 19:37 - Recap and Acknowledgments 20:53 - TEM as a Transformer network 21:55 - Brilliant 23:19 - Outro REFERENCES: 1. Whittington, J. C. R. et al. The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation. Cell 183, 1249-1263.e23 (2020). 2. Whittington, J. C. R., Warren, J. & Behrens, T. E. J. Relating transformers to models and neural representations of the hippocampal formation. Preprint at http://arxiv.org/abs/2112.04035 (2022). 3. Whittington, J. C. R., McCaffary, D., Bakermans, J. J. W. & Behrens, T. E. J. How to build a cognitive map. Nat Neurosci 25, 1257–1272 (2022). CREDITS: Icons by biorender.com and freepik.com Brain 3D models were created with Blender software using publicly available BrainGlobe atlases (https://brainglobe.info/atlas-api) Animations were made using open-source Python packages Matplotlib and RatInABox ( https://github.com/TomGeorge1234/RatInABox ) Rat free 3D model: https://skfb.ly/oEq7y This video was sponsored by Brilliant