Remapping in a recurrent neural network model of navigation and context inference
Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns (‘remap’) in response to changing contextual factors such as environmental cues, task conditions, and behavioral states,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2023-07-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/86943 |
| _version_ | 1797785407789727744 |
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| author | Isabel IC Low Lisa M Giocomo Alex H Williams |
| author_facet | Isabel IC Low Lisa M Giocomo Alex H Williams |
| author_sort | Isabel IC Low |
| collection | DOAJ |
| description | Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns (‘remap’) in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference. |
| first_indexed | 2024-03-13T00:53:40Z |
| format | Article |
| id | doaj.art-e3d381af2aa44529a74750f14ac54579 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-03-13T00:53:40Z |
| publishDate | 2023-07-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-e3d381af2aa44529a74750f14ac545792023-07-07T09:02:09ZengeLife Sciences Publications LtdeLife2050-084X2023-07-011210.7554/eLife.86943Remapping in a recurrent neural network model of navigation and context inferenceIsabel IC Low0https://orcid.org/0000-0001-6465-8459Lisa M Giocomo1https://orcid.org/0000-0003-0416-2528Alex H Williams2https://orcid.org/0000-0001-5853-103XZuckerman Mind Brain Behavior Institute, Columbia University, New York, United StatesDepartment of Neurobiology, Stanford University, Stanford, United StatesCenter for Computational Neuroscience, Flatiron Institute, New York, United States; Center for Neural Science, New York University, New York, United StatesNeurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns (‘remap’) in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.https://elifesciences.org/articles/86943Recurrent neural network modelsdynamic codinglatent stateattractor manifoldsmedial entorhinal cortexnavigation |
| spellingShingle | Isabel IC Low Lisa M Giocomo Alex H Williams Remapping in a recurrent neural network model of navigation and context inference eLife Recurrent neural network models dynamic coding latent state attractor manifolds medial entorhinal cortex navigation |
| title | Remapping in a recurrent neural network model of navigation and context inference |
| title_full | Remapping in a recurrent neural network model of navigation and context inference |
| title_fullStr | Remapping in a recurrent neural network model of navigation and context inference |
| title_full_unstemmed | Remapping in a recurrent neural network model of navigation and context inference |
| title_short | Remapping in a recurrent neural network model of navigation and context inference |
| title_sort | remapping in a recurrent neural network model of navigation and context inference |
| topic | Recurrent neural network models dynamic coding latent state attractor manifolds medial entorhinal cortex navigation |
| url | https://elifesciences.org/articles/86943 |
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