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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.
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