A tale of two algorithms: Structured slots explain prefrontal sequence memory and are unified with hippocampal cognitive maps.

Autor:	Whittington JCR; Department of Applied Physics, Stanford University, Palo Alto, CA, USA; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK. Electronic address: jcrwhittington@gmail.com., Dorrell W; Gatsby Computational Neuroscience Unit, University College London, London, UK., Behrens TEJ; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK., Ganguli S; Department of Applied Physics, Stanford University, Palo Alto, CA, USA., El-Gaby M; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
Jazyk:	angličtina
Zdroj:	Neuron [Neuron] 2024 Nov 15. Date of Electronic Publication: 2024 Nov 15.
DOI:	10.1016/j.neuron.2024.10.017
Abstrakt:	Remembering events is crucial to intelligent behavior. Flexible memory retrieval requires a cognitive map and is supported by two key brain systems: hippocampal episodic memory (EM) and prefrontal working memory (WM). Although an understanding of EM is emerging, little is understood of WM beyond simple memory retrieval. We develop a mathematical theory relating the algorithms and representations of EM and WM by unveiling a duality between storing memories in synapses versus neural activity. This results in a formalism of prefrontal WM as structured, controllable neural subspaces (activity slots) representing dynamic cognitive maps without synaptic plasticity. Using neural networks, we elucidate differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that prefrontal representations in tasks from list learning to cue-dependent recall are unified as controllable activity slots. Our results unify frontal and temporal representations of memory and offer a new understanding for dynamic prefrontal representations of WM. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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