Learning recurrent dynamics in spiking networks
Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifyin...
| Những tác giả chính: | , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
| Được phát hành: |
eLife Sciences Publications Ltd
2018-09-01
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| Loạt: | eLife |
| Những chủ đề: | |
| Truy cập trực tuyến: | https://elifesciences.org/articles/37124 |
| _version_ | 1828168971185029120 |
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| author | Christopher M Kim Carson C Chow |
| author_facet | Christopher M Kim Carson C Chow |
| author_sort | Christopher M Kim |
| collection | DOAJ |
| description | Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity in a network of excitatory and inhibitory neurons respecting Dale’s law, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain. |
| first_indexed | 2024-04-12T02:43:39Z |
| format | Article |
| id | doaj.art-e38b8dc2db4449f7a71b51a2cc3aa65c |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T02:43:39Z |
| publishDate | 2018-09-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-e38b8dc2db4449f7a71b51a2cc3aa65c2022-12-22T03:51:15ZengeLife Sciences Publications LtdeLife2050-084X2018-09-01710.7554/eLife.37124Learning recurrent dynamics in spiking networksChristopher M Kim0https://orcid.org/0000-0002-1322-6207Carson C Chow1https://orcid.org/0000-0003-1463-9553Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, United StatesLaboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, United StatesSpiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity in a network of excitatory and inhibitory neurons respecting Dale’s law, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.https://elifesciences.org/articles/37124spiking networkrecurrent dynamicslearninguniversal dynamics |
| spellingShingle | Christopher M Kim Carson C Chow Learning recurrent dynamics in spiking networks eLife spiking network recurrent dynamics learning universal dynamics |
| title | Learning recurrent dynamics in spiking networks |
| title_full | Learning recurrent dynamics in spiking networks |
| title_fullStr | Learning recurrent dynamics in spiking networks |
| title_full_unstemmed | Learning recurrent dynamics in spiking networks |
| title_short | Learning recurrent dynamics in spiking networks |
| title_sort | learning recurrent dynamics in spiking networks |
| topic | spiking network recurrent dynamics learning universal dynamics |
| url | https://elifesciences.org/articles/37124 |
| work_keys_str_mv | AT christophermkim learningrecurrentdynamicsinspikingnetworks AT carsoncchow learningrecurrentdynamicsinspikingnetworks |