Sparse spectral estimation from point process observations
We consider the problem of estimating the power spectral density of the neural covariates underlying the spiking of a neuronal population. We assume the spiking of the neuronal ensemble to be described by Bernoulli statistics. Furthermore, we consider the conditional intensity function to be the log...
| Main Authors: | , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/112115 https://orcid.org/0000-0003-2668-7819 |
| _version_ | 1811077519214903296 |
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| author | Miran, Sina Purdon, Patrick L. Babadi, Behtash Brown, Emery Neal |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science Miran, Sina Purdon, Patrick L. Babadi, Behtash Brown, Emery Neal |
| author_sort | Miran, Sina |
| collection | MIT |
| description | We consider the problem of estimating the power spectral density of the neural covariates underlying the spiking of a neuronal population. We assume the spiking of the neuronal ensemble to be described by Bernoulli statistics. Furthermore, we consider the conditional intensity function to be the logistic map of a second-order stationary process with sparse frequency content. Using the binary spiking data recorded from the population, we calculate the maximum a posteriori estimate of the power spectral density of the process while enforcing sparsity-promoting priors on the estimate. Using both simulated and clinically recorded data, we show that our method outperforms the existing methods for extracting a frequency domain representation from the spiking data of a neuronal population. |
| first_indexed | 2024-09-23T10:44:19Z |
| format | Article |
| id | mit-1721.1/112115 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:44:19Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1121152022-09-27T14:39:12Z Sparse spectral estimation from point process observations Miran, Sina Purdon, Patrick L. Babadi, Behtash Brown, Emery Neal Institute for Medical Engineering and Science Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery Neal We consider the problem of estimating the power spectral density of the neural covariates underlying the spiking of a neuronal population. We assume the spiking of the neuronal ensemble to be described by Bernoulli statistics. Furthermore, we consider the conditional intensity function to be the logistic map of a second-order stationary process with sparse frequency content. Using the binary spiking data recorded from the population, we calculate the maximum a posteriori estimate of the power spectral density of the process while enforcing sparsity-promoting priors on the estimate. Using both simulated and clinically recorded data, we show that our method outperforms the existing methods for extracting a frequency domain representation from the spiking data of a neuronal population. 2017-11-01T18:04:08Z 2017-11-01T18:04:08Z 2017-06 2017-03 2017-10-26T17:53:18Z Article http://purl.org/eprint/type/ConferencePaper 978-1-5090-4117-6 2379-190X http://hdl.handle.net/1721.1/112115 Miran, Sina, Patrick L. Purdon, Emery N. Brown, and Behtash Babadi. “Sparse Spectral Estimation from Point Process Observations.” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 5-9 2017, New Orleans, LA, USA, Institute of Electrical and Electronics Engineers (IEEE) June 2017 © 2017 Institute of Electrical and Electronics Engineers (IEEE) https://orcid.org/0000-0003-2668-7819 http://dx.doi.org/10.1109/ICASSP.2017.7952274 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other univ. web domain |
| spellingShingle | Miran, Sina Purdon, Patrick L. Babadi, Behtash Brown, Emery Neal Sparse spectral estimation from point process observations |
| title | Sparse spectral estimation from point process observations |
| title_full | Sparse spectral estimation from point process observations |
| title_fullStr | Sparse spectral estimation from point process observations |
| title_full_unstemmed | Sparse spectral estimation from point process observations |
| title_short | Sparse spectral estimation from point process observations |
| title_sort | sparse spectral estimation from point process observations |
| url | http://hdl.handle.net/1721.1/112115 https://orcid.org/0000-0003-2668-7819 |
| work_keys_str_mv | AT miransina sparsespectralestimationfrompointprocessobservations AT purdonpatrickl sparsespectralestimationfrompointprocessobservations AT babadibehtash sparsespectralestimationfrompointprocessobservations AT brownemeryneal sparsespectralestimationfrompointprocessobservations |