Artificial intelligence insights into hippocampal processing
<jats:p>Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information abo...
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| Format: | Article |
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Frontiers Media SA
2022
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| Online Access: | https://hdl.handle.net/1721.1/146562 |
| _version_ | 1826194511257141248 |
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| author | Wirtshafter, Hannah S. Wilson, Matthew A. |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Wirtshafter, Hannah S. Wilson, Matthew A. |
| author_sort | Wirtshafter, Hannah S. |
| collection | MIT |
| description | <jats:p>Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space. To perform, muZero uses only three functions that are general yet specific enough to allow learning across a variety of tasks without overgeneralization across different contexts. Similarly, humans and animals are able to learn and improve in complex environments while transferring learning from other contexts and without overgeneralizing. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. Like muZero, the eHPCS is also able to adjust contextual representations depending on the degree and significance of environmental changes and environmental cues. In this opinion, we will argue that the muZero functions parallel those of the hippocampal system. We will show that the different components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cell representations occur between similar and distinct contexts can be informed by advances in artificial intelligence agents such as muZero. We additionally explain how advances in AI agents will provide frameworks and predictions by which to investigate the expected link between state changes and neuronal firing. Specifically, we will discuss testable predictions about the eHPCS, including the functions of replay and remapping, informed by the mechanisms behind muZero learning. We conclude with additional ways in which agents such as muZero can aid in illuminating prospective questions about neural functioning, as well as how these agents may shed light on potential expected answers.</jats:p> |
| first_indexed | 2024-09-23T09:57:14Z |
| format | Article |
| id | mit-1721.1/146562 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:57:14Z |
| publishDate | 2022 |
| publisher | Frontiers Media SA |
| record_format | dspace |
| spelling | mit-1721.1/1465622023-02-16T16:18:27Z Artificial intelligence insights into hippocampal processing Wirtshafter, Hannah S. Wilson, Matthew A. Massachusetts Institute of Technology. Department of Biology Picower Institute for Learning and Memory Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Cellular and Molecular Neuroscience Neuroscience (miscellaneous) <jats:p>Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space. To perform, muZero uses only three functions that are general yet specific enough to allow learning across a variety of tasks without overgeneralization across different contexts. Similarly, humans and animals are able to learn and improve in complex environments while transferring learning from other contexts and without overgeneralizing. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. Like muZero, the eHPCS is also able to adjust contextual representations depending on the degree and significance of environmental changes and environmental cues. In this opinion, we will argue that the muZero functions parallel those of the hippocampal system. We will show that the different components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cell representations occur between similar and distinct contexts can be informed by advances in artificial intelligence agents such as muZero. We additionally explain how advances in AI agents will provide frameworks and predictions by which to investigate the expected link between state changes and neuronal firing. Specifically, we will discuss testable predictions about the eHPCS, including the functions of replay and remapping, informed by the mechanisms behind muZero learning. We conclude with additional ways in which agents such as muZero can aid in illuminating prospective questions about neural functioning, as well as how these agents may shed light on potential expected answers.</jats:p> 2022-11-21T16:04:29Z 2022-11-21T16:04:29Z 2022-11-07 Article http://purl.org/eprint/type/JournalArticle 1662-5188 https://hdl.handle.net/1721.1/146562 Wirtshafter, Hannah S. and Wilson, Matthew A. 2022. "Artificial intelligence insights into hippocampal processing." 16. 10.3389/fncom.2022.1044659 Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Media SA Frontiers |
| spellingShingle | Cellular and Molecular Neuroscience Neuroscience (miscellaneous) Wirtshafter, Hannah S. Wilson, Matthew A. Artificial intelligence insights into hippocampal processing |
| title | Artificial intelligence insights into hippocampal processing |
| title_full | Artificial intelligence insights into hippocampal processing |
| title_fullStr | Artificial intelligence insights into hippocampal processing |
| title_full_unstemmed | Artificial intelligence insights into hippocampal processing |
| title_short | Artificial intelligence insights into hippocampal processing |
| title_sort | artificial intelligence insights into hippocampal processing |
| topic | Cellular and Molecular Neuroscience Neuroscience (miscellaneous) |
| url | https://hdl.handle.net/1721.1/146562 |
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