Elucidating the selection mechanisms in context-dependent computation through low-rank neural network modeling
Humans and animals exhibit a remarkable ability to selectively filter out irrelevant information based on context. However, the neural mechanisms underlying this context-dependent selection process remain elusive. Recently, the issue of discriminating between two prevalent selection mechanisms—input...
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eLife Sciences Publications Ltd
2025-07-01
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| Online Access: | https://elifesciences.org/articles/103636 |
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| author | Yiteng Zhang Jianfeng Feng Bin Min |
| author_facet | Yiteng Zhang Jianfeng Feng Bin Min |
| author_sort | Yiteng Zhang |
| collection | DOAJ |
| description | Humans and animals exhibit a remarkable ability to selectively filter out irrelevant information based on context. However, the neural mechanisms underlying this context-dependent selection process remain elusive. Recently, the issue of discriminating between two prevalent selection mechanisms—input modulation versus selection vector modulation—with neural activity data has been highlighted as one of the major challenges in the study of individual variability underlying context-dependent decision-making (CDM). Here, we investigated these selection mechanisms through low-rank neural network modeling of the CDM task. We first showed that only input modulation was allowed in rank-one neural networks and additional dimensions of network connectivity were required to endow neural networks with selection vector modulation. Through rigorous information flow analysis, we gained a mechanistic understanding of why additional dimensions are required for selection vector modulation and how additional dimensions specifically contribute to selection vector modulation. This new understanding then led to the identification of novel neural dynamical signatures for selection vector modulation at both single neuron and population levels. Together, our results provide a rigorous theoretical framework linking network connectivity, neural dynamics, and selection mechanisms, paving the way towards elucidating the circuit mechanisms when studying individual variability in context-dependent computation. |
| format | Article |
| id | doaj-art-af53dd99b1564165b759d6e7c7166f3b |
| institution | DOAJ |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | eLife Sciences Publications Ltd |
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| series | eLife |
| spelling | doaj-art-af53dd99b1564165b759d6e7c7166f3b2025-08-20T02:43:55ZengeLife Sciences Publications LtdeLife2050-084X2025-07-011310.7554/eLife.103636Elucidating the selection mechanisms in context-dependent computation through low-rank neural network modelingYiteng Zhang0https://orcid.org/0009-0001-5192-521XJianfeng Feng1https://orcid.org/0000-0001-5987-2258Bin Min2https://orcid.org/0000-0003-1006-9629School of Data Science, Fudan University, Shanghai, China; Lingang Laboratory, Shanghai, ChinaSchool of Data Science, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, ChinaLingang Laboratory, Shanghai, ChinaHumans and animals exhibit a remarkable ability to selectively filter out irrelevant information based on context. However, the neural mechanisms underlying this context-dependent selection process remain elusive. Recently, the issue of discriminating between two prevalent selection mechanisms—input modulation versus selection vector modulation—with neural activity data has been highlighted as one of the major challenges in the study of individual variability underlying context-dependent decision-making (CDM). Here, we investigated these selection mechanisms through low-rank neural network modeling of the CDM task. We first showed that only input modulation was allowed in rank-one neural networks and additional dimensions of network connectivity were required to endow neural networks with selection vector modulation. Through rigorous information flow analysis, we gained a mechanistic understanding of why additional dimensions are required for selection vector modulation and how additional dimensions specifically contribute to selection vector modulation. This new understanding then led to the identification of novel neural dynamical signatures for selection vector modulation at both single neuron and population levels. Together, our results provide a rigorous theoretical framework linking network connectivity, neural dynamics, and selection mechanisms, paving the way towards elucidating the circuit mechanisms when studying individual variability in context-dependent computation.https://elifesciences.org/articles/103636context-dependent computationdecision makingneural dynamicscognitive controllow-rank RNNindividual variability |
| spellingShingle | Yiteng Zhang Jianfeng Feng Bin Min Elucidating the selection mechanisms in context-dependent computation through low-rank neural network modeling eLife context-dependent computation decision making neural dynamics cognitive control low-rank RNN individual variability |
| title | Elucidating the selection mechanisms in context-dependent computation through low-rank neural network modeling |
| title_full | Elucidating the selection mechanisms in context-dependent computation through low-rank neural network modeling |
| title_fullStr | Elucidating the selection mechanisms in context-dependent computation through low-rank neural network modeling |
| title_full_unstemmed | Elucidating the selection mechanisms in context-dependent computation through low-rank neural network modeling |
| title_short | Elucidating the selection mechanisms in context-dependent computation through low-rank neural network modeling |
| title_sort | elucidating the selection mechanisms in context dependent computation through low rank neural network modeling |
| topic | context-dependent computation decision making neural dynamics cognitive control low-rank RNN individual variability |
| url | https://elifesciences.org/articles/103636 |
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