Sparse connectivity for MAP inference in linear models using sister mitral cells.
Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is to understand how the brain extracts those variables. Here we revisit a common encoding model in which variables are encoded linearly...
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| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009808&type=printable |
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| author | Sina Tootoonian Andreas T Schaefer Peter E Latham |
| author_facet | Sina Tootoonian Andreas T Schaefer Peter E Latham |
| author_sort | Sina Tootoonian |
| collection | DOAJ |
| description | Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is to understand how the brain extracts those variables. Here we revisit a common encoding model in which variables are encoded linearly. Although there are typically more variables than neurons, this problem is still solvable because only a small number of variables appear at any one time (sparse prior). However, previous solutions require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose an algorithm that provably reaches the MAP (maximum a posteriori) inference solution, but does so using sparse connectivity. Our algorithm is inspired by the circuit of the mouse olfactory bulb, but our approach is general enough to apply to other modalities. In addition, it should be possible to extend it to nonlinear encoding models. |
| format | Article |
| id | doaj-art-673a2675b01944a0bbd8e32c6822f353 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-673a2675b01944a0bbd8e32c6822f3532025-08-20T03:44:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-01-01181e100980810.1371/journal.pcbi.1009808Sparse connectivity for MAP inference in linear models using sister mitral cells.Sina TootoonianAndreas T SchaeferPeter E LathamSensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is to understand how the brain extracts those variables. Here we revisit a common encoding model in which variables are encoded linearly. Although there are typically more variables than neurons, this problem is still solvable because only a small number of variables appear at any one time (sparse prior). However, previous solutions require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose an algorithm that provably reaches the MAP (maximum a posteriori) inference solution, but does so using sparse connectivity. Our algorithm is inspired by the circuit of the mouse olfactory bulb, but our approach is general enough to apply to other modalities. In addition, it should be possible to extend it to nonlinear encoding models.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009808&type=printable |
| spellingShingle | Sina Tootoonian Andreas T Schaefer Peter E Latham Sparse connectivity for MAP inference in linear models using sister mitral cells. PLoS Computational Biology |
| title | Sparse connectivity for MAP inference in linear models using sister mitral cells. |
| title_full | Sparse connectivity for MAP inference in linear models using sister mitral cells. |
| title_fullStr | Sparse connectivity for MAP inference in linear models using sister mitral cells. |
| title_full_unstemmed | Sparse connectivity for MAP inference in linear models using sister mitral cells. |
| title_short | Sparse connectivity for MAP inference in linear models using sister mitral cells. |
| title_sort | sparse connectivity for map inference in linear models using sister mitral cells |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009808&type=printable |
| work_keys_str_mv | AT sinatootoonian sparseconnectivityformapinferenceinlinearmodelsusingsistermitralcells AT andreastschaefer sparseconnectivityformapinferenceinlinearmodelsusingsistermitralcells AT peterelatham sparseconnectivityformapinferenceinlinearmodelsusingsistermitralcells |