Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells
A goal of systems neuroscience is to discover the circuit mechanisms underlying brain function. Despite experimental advances that enable circuit-wide neural recording, the problem remains open in part because solving the ‘inverse problem’ of inferring circuity and mechanism by merely observing acti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2018-07-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/33503 |
| _version_ | 1811200791004839936 |
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| author | John Widloski Michael P Marder Ila R Fiete |
| author_facet | John Widloski Michael P Marder Ila R Fiete |
| author_sort | John Widloski |
| collection | DOAJ |
| description | A goal of systems neuroscience is to discover the circuit mechanisms underlying brain function. Despite experimental advances that enable circuit-wide neural recording, the problem remains open in part because solving the ‘inverse problem’ of inferring circuity and mechanism by merely observing activity is hard. In the grid cell system, we show through modeling that a technique based on global circuit perturbation and examination of a novel theoretical object called the distribution of relative phase shifts (DRPS) could reveal the mechanisms of a cortical circuit at unprecedented detail using extremely sparse neural recordings. We establish feasibility, showing that the method can discriminate between recurrent versus feedforward mechanisms and amongst various recurrent mechanisms using recordings from a handful of cells. The proposed strategy demonstrates that sparse recording coupled with simple perturbation can reveal more about circuit mechanism than can full knowledge of network activity or the synaptic connectivity matrix. |
| first_indexed | 2024-04-12T02:09:37Z |
| format | Article |
| id | doaj.art-f4ca2c9348a549d688708f0e42e7b1c8 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T02:09:37Z |
| publishDate | 2018-07-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-f4ca2c9348a549d688708f0e42e7b1c82022-12-22T03:52:27ZengeLife Sciences Publications LtdeLife2050-084X2018-07-01710.7554/eLife.33503Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cellsJohn Widloski0https://orcid.org/0000-0003-4236-8957Michael P Marder1Ila R Fiete2https://orcid.org/0000-0003-4738-2539Department of Psychology, The University of California, Berkeley, United StatesDepartment of Physics, The University of Texas, Austin, United StatesDepartment of Physics, The University of Texas, Austin, United States; Center for Learning and Memory, The University of Texas, Austin, United StatesA goal of systems neuroscience is to discover the circuit mechanisms underlying brain function. Despite experimental advances that enable circuit-wide neural recording, the problem remains open in part because solving the ‘inverse problem’ of inferring circuity and mechanism by merely observing activity is hard. In the grid cell system, we show through modeling that a technique based on global circuit perturbation and examination of a novel theoretical object called the distribution of relative phase shifts (DRPS) could reveal the mechanisms of a cortical circuit at unprecedented detail using extremely sparse neural recordings. We establish feasibility, showing that the method can discriminate between recurrent versus feedforward mechanisms and amongst various recurrent mechanisms using recordings from a handful of cells. The proposed strategy demonstrates that sparse recording coupled with simple perturbation can reveal more about circuit mechanism than can full knowledge of network activity or the synaptic connectivity matrix.https://elifesciences.org/articles/33503grid cellscircuit perturbationattractor dynamicsrecurrent networks |
| spellingShingle | John Widloski Michael P Marder Ila R Fiete Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells eLife grid cells circuit perturbation attractor dynamics recurrent networks |
| title | Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells |
| title_full | Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells |
| title_fullStr | Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells |
| title_full_unstemmed | Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells |
| title_short | Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells |
| title_sort | inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells |
| topic | grid cells circuit perturbation attractor dynamics recurrent networks |
| url | https://elifesciences.org/articles/33503 |
| work_keys_str_mv | AT johnwidloski inferringcircuitmechanismsfromsparseneuralrecordingandglobalperturbationingridcells AT michaelpmarder inferringcircuitmechanismsfromsparseneuralrecordingandglobalperturbationingridcells AT ilarfiete inferringcircuitmechanismsfromsparseneuralrecordingandglobalperturbationingridcells |