Achieving stable dynamics in neural circuits
© 2020 Kozachkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The brain consists of many interconnecte...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135248 |
| _version_ | 1826214246683246592 |
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| author | Kozachkov, Leo Lundqvist, Mikael Slotine, Jean-Jacques Miller, Earl K |
| author2 | Picower Institute for Learning and Memory |
| author_facet | Picower Institute for Learning and Memory Kozachkov, Leo Lundqvist, Mikael Slotine, Jean-Jacques Miller, Earl K |
| author_sort | Kozachkov, Leo |
| collection | MIT |
| description | © 2020 Kozachkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity. Crucially, our analysis is not limited to analyzing the stability of fixed geometric objects in state space (e.g points, lines, planes), but rather the stability of state trajectories which may be complex and time-varying. |
| first_indexed | 2024-09-23T16:02:27Z |
| format | Article |
| id | mit-1721.1/135248 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:02:27Z |
| publishDate | 2021 |
| publisher | Public Library of Science (PLoS) |
| record_format | dspace |
| spelling | mit-1721.1/1352482023-03-24T19:14:43Z Achieving stable dynamics in neural circuits Kozachkov, Leo Lundqvist, Mikael Slotine, Jean-Jacques Miller, Earl K Picower Institute for Learning and Memory Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Massachusetts Institute of Technology. Nonlinear Systems Laboratory © 2020 Kozachkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity. Crucially, our analysis is not limited to analyzing the stability of fixed geometric objects in state space (e.g points, lines, planes), but rather the stability of state trajectories which may be complex and time-varying. 2021-10-27T20:22:38Z 2021-10-27T20:22:38Z 2020 2021-03-24T17:04:18Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135248 en 10.1371/JOURNAL.PCBI.1007659 PLoS Computational Biology Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science (PLoS) PLoS |
| spellingShingle | Kozachkov, Leo Lundqvist, Mikael Slotine, Jean-Jacques Miller, Earl K Achieving stable dynamics in neural circuits |
| title | Achieving stable dynamics in neural circuits |
| title_full | Achieving stable dynamics in neural circuits |
| title_fullStr | Achieving stable dynamics in neural circuits |
| title_full_unstemmed | Achieving stable dynamics in neural circuits |
| title_short | Achieving stable dynamics in neural circuits |
| title_sort | achieving stable dynamics in neural circuits |
| url | https://hdl.handle.net/1721.1/135248 |
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