Estimating a Separably Markov Random Field from Binary Observations
A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trial...
| Main Authors: | , , , , |
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| Format: | Article |
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MIT Press
2018
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| Online Access: | http://hdl.handle.net/1721.1/115400 https://orcid.org/0000-0002-0438-3163 |
| _version_ | 1826215043780313088 |
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| author | Zhang, Yingzhuo Malem-Shinitski, Noa Ba, Demba Allsop, Stephen Azariah Tye, Kay M |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Zhang, Yingzhuo Malem-Shinitski, Noa Ba, Demba Allsop, Stephen Azariah Tye, Kay M |
| author_sort | Zhang, Yingzhuo |
| collection | MIT |
| description | A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trials, which may cause the loss of information pertinent to understanding the function of a neuron or circuit. We introduce a new method that can determine not only the trial-to-trial dynamics that accompany the learning of a contingency by a neuron, but also the latency of this learning with respect to the onset of a conditioned stimulus. The backbone of the method is a separable two-dimensional (2D) random field (RF) model of neural spike rasters, in which the joint conditional intensity function of a neuron over time and trials depends on two latent Markovian state sequences that evolve separately but in parallel. Classical tools to estimate state-space models cannot be applied readily to our 2D separable RF model. We develop efficient statistical and computational tools to estimate the parameters of the separable 2D RF model. We apply these to data collected from neurons in the prefrontal cortex in an experiment designed to characterize the neural underpinnings of the associative learning of fear in mice. Overall, the separable 2D RF model provides a detailed, interpretable characterization of the dynamics of neural spiking that accompany the learning of a contingency. |
| first_indexed | 2024-09-23T16:15:37Z |
| format | Article |
| id | mit-1721.1/115400 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:15:37Z |
| publishDate | 2018 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/1154002022-09-29T19:14:05Z Estimating a Separably Markov Random Field from Binary Observations Zhang, Yingzhuo Malem-Shinitski, Noa Ba, Demba Allsop, Stephen Azariah Tye, Kay M Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Picower Institute for Learning and Memory Allsop, Stephen Azariah Tye, Kay M A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trials, which may cause the loss of information pertinent to understanding the function of a neuron or circuit. We introduce a new method that can determine not only the trial-to-trial dynamics that accompany the learning of a contingency by a neuron, but also the latency of this learning with respect to the onset of a conditioned stimulus. The backbone of the method is a separable two-dimensional (2D) random field (RF) model of neural spike rasters, in which the joint conditional intensity function of a neuron over time and trials depends on two latent Markovian state sequences that evolve separately but in parallel. Classical tools to estimate state-space models cannot be applied readily to our 2D separable RF model. We develop efficient statistical and computational tools to estimate the parameters of the separable 2D RF model. We apply these to data collected from neurons in the prefrontal cortex in an experiment designed to characterize the neural underpinnings of the associative learning of fear in mice. Overall, the separable 2D RF model provides a detailed, interpretable characterization of the dynamics of neural spiking that accompany the learning of a contingency. National Institute of Mental Health (U.S.) (Grant R01-MH102441-01) National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Award DP2-DK-102256-01) National Institute on Aging (Grant RF1- AG047661-01) 2018-05-16T16:50:28Z 2018-05-16T16:50:28Z 2018-03 2018-05-04T16:53:26Z Article http://purl.org/eprint/type/JournalArticle 0899-7667 1530-888X http://hdl.handle.net/1721.1/115400 Zhang, Yingzhuo et al. “Estimating a Separably Markov Random Field from Binary Observations.” Neural Computation 30, 4 (April 2018): 1046–1079 © 2018 Massachusetts Institute of Technology https://orcid.org/0000-0002-0438-3163 http://dx.doi.org/10.1162/neco_a_01059 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 Massachusetts Institute of Technology Press |
| spellingShingle | Zhang, Yingzhuo Malem-Shinitski, Noa Ba, Demba Allsop, Stephen Azariah Tye, Kay M Estimating a Separably Markov Random Field from Binary Observations |
| title | Estimating a Separably Markov Random Field from Binary Observations |
| title_full | Estimating a Separably Markov Random Field from Binary Observations |
| title_fullStr | Estimating a Separably Markov Random Field from Binary Observations |
| title_full_unstemmed | Estimating a Separably Markov Random Field from Binary Observations |
| title_short | Estimating a Separably Markov Random Field from Binary Observations |
| title_sort | estimating a separably markov random field from binary observations |
| url | http://hdl.handle.net/1721.1/115400 https://orcid.org/0000-0002-0438-3163 |
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