A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields
Pyramidal neurons recorded from the rat hippocampus and entorhinal cortex, such as place and grid cells, have diverse receptive fields, which are either unimodal or multimodal. Spiking activity from these cells encodes information about the spatial position of a freely foraging rat. At fine timescal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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MIT Press
2016
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| Online Access: | http://hdl.handle.net/1721.1/103679 https://orcid.org/0000-0001-7149-3584 |
| _version_ | 1826199098519191552 |
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| author | Agarwal, Rahul Chen, Zhe Kloosterman, Fabian Wilson, Matthew A. Sarma, Sridevi V. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Agarwal, Rahul Chen, Zhe Kloosterman, Fabian Wilson, Matthew A. Sarma, Sridevi V. |
| author_sort | Agarwal, Rahul |
| collection | MIT |
| description | Pyramidal neurons recorded from the rat hippocampus and entorhinal cortex, such as place and grid cells, have diverse receptive fields, which are either unimodal or multimodal. Spiking activity from these cells encodes information about the spatial position of a freely foraging rat. At fine timescales, a neuron’s spike activity also depends significantly on its own spike history. However, due to limitations of current parametric modeling approaches, it remains a challenge to estimate complex, multimodal neuronal receptive fields while incorporating spike history dependence. Furthermore, efforts to decode the rat’s trajectory in one- or two-dimensional space from hippocampal ensemble spiking activity have mainly focused on spike history–independent neuronal encoding models. In this letter, we address these two important issues by extending a recently introduced nonparametric neural encoding framework that allows modeling both complex spatial receptive fields and spike history dependencies. Using this extended nonparametric approach, we develop novel algorithms for decoding a rat’s trajectory based on recordings of hippocampal place cells and entorhinal grid cells. Results show that both encoding and decoding models derived from our new method performed significantly better than state-of-the-art encoding and decoding models on 6 minutes of test data. In addition, our model’s performance remains invariant to the apparent modality of the neuron’s receptive field. |
| first_indexed | 2024-09-23T11:14:23Z |
| format | Article |
| id | mit-1721.1/103679 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:14:23Z |
| publishDate | 2016 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/1036792022-09-27T18:07:30Z A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields Agarwal, Rahul Chen, Zhe Kloosterman, Fabian Wilson, Matthew A. Sarma, Sridevi V. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Picower Institute for Learning and Memory Agarwal, Rahul Chen, Zhe Kloosterman, Fabian Wilson, Matthew A. Sarma, Sridevi V. Pyramidal neurons recorded from the rat hippocampus and entorhinal cortex, such as place and grid cells, have diverse receptive fields, which are either unimodal or multimodal. Spiking activity from these cells encodes information about the spatial position of a freely foraging rat. At fine timescales, a neuron’s spike activity also depends significantly on its own spike history. However, due to limitations of current parametric modeling approaches, it remains a challenge to estimate complex, multimodal neuronal receptive fields while incorporating spike history dependence. Furthermore, efforts to decode the rat’s trajectory in one- or two-dimensional space from hippocampal ensemble spiking activity have mainly focused on spike history–independent neuronal encoding models. In this letter, we address these two important issues by extending a recently introduced nonparametric neural encoding framework that allows modeling both complex spatial receptive fields and spike history dependencies. Using this extended nonparametric approach, we develop novel algorithms for decoding a rat’s trajectory based on recordings of hippocampal place cells and entorhinal grid cells. Results show that both encoding and decoding models derived from our new method performed significantly better than state-of-the-art encoding and decoding models on 6 minutes of test data. In addition, our model’s performance remains invariant to the apparent modality of the neuron’s receptive field. National Science Foundation (U.S.) (NSF-CRCNS award (no. 1307645)) National Institutes of Health (U.S.) (NIH grant R01-MH06197) National Institutes of Health (U.S.) (NIH grant TR01-GM10498) United States. Office of Naval Research (ONR-MURI grant N00014-10-1-0936) National Science Foundation (U.S.) (CAREER Award 1055560) Burroughs Wellcome Fund (CASI Award 1007274) National Science Foundation (U.S.) (NSF EFRI-M3C) 2016-07-18T19:20:42Z 2016-07-18T19:20:42Z 2016-06 Article http://purl.org/eprint/type/JournalArticle 0899-7667 1530-888X http://hdl.handle.net/1721.1/103679 Agarwal, Rahul, Zhe Chen, Fabian Kloosterman, Matthew A. Wilson, and Sridevi V. Sarma. “A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields.” Neural Computation 28, no. 7 (July 2016): 1356–1387. https://orcid.org/0000-0001-7149-3584 en_US http://dx.doi.org/10.1162/NECO_a_00847 Neural Computation Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf MIT Press MIT Press |
| spellingShingle | Agarwal, Rahul Chen, Zhe Kloosterman, Fabian Wilson, Matthew A. Sarma, Sridevi V. A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields |
| title | A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields |
| title_full | A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields |
| title_fullStr | A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields |
| title_full_unstemmed | A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields |
| title_short | A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields |
| title_sort | novel nonparametric approach for neural encoding and decoding models of multimodal receptive fields |
| url | http://hdl.handle.net/1721.1/103679 https://orcid.org/0000-0001-7149-3584 |
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