Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations
As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modeling such data must also be developed. Here, we present a model for the type of data commonly recorded in early sensory pathways: responses to repeated trials...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2010-11-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00144/full |
| _version_ | 1819206067994755072 |
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| author | Dmitry R Lyamzin Dmitry R Lyamzin Jakob H Macke Jakob H Macke Nicholas A Lesica Nicholas A Lesica |
| author_facet | Dmitry R Lyamzin Dmitry R Lyamzin Jakob H Macke Jakob H Macke Nicholas A Lesica Nicholas A Lesica |
| author_sort | Dmitry R Lyamzin |
| collection | DOAJ |
| description | As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modeling such data must also be developed. Here, we present a model for the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate (as described by its PSTH) and the dependencies between cells are characterized by both signal and noise correlations. This model is an extension of previous attempts to model population spike trains designed to control only the total correlation between cells. In our model, the response of each cell is represented as a binary vector given by the dichotomized sum of a deterministic ‘signal’ that is repeated on each trial and a Gaussian random ‘noise’ that is different on each trial. This model allows the simulation of population spike trains with PSTHs, trial-to-trial variability, and pairwise correlations that match those measured experimentally. Furthermore, the model also allows the noise correlations in the spike trains to be manipulated independently of the signal correlations and single cell properties. To demonstrate the utility of the model, we use it to simulate and manipulate experimental responses from the mammalian auditory and visual systems. We also present a general form of the model in which both the signal and noise are Gaussian random processes, allowing the mean spike rate, trial-to-trial variability, and pairwise signal and noise correlations to be specified independently. Together, these methods for modeling spike trains comprise a potentially powerful set of tools for both theorists and experimentalists studying population responses in sensory systems. |
| first_indexed | 2024-12-23T05:01:43Z |
| format | Article |
| id | doaj.art-cf356e626f9a4038af30ac1a5a5ef55d |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-23T05:01:43Z |
| publishDate | 2010-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-cf356e626f9a4038af30ac1a5a5ef55d2022-12-21T17:59:13ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882010-11-01410.3389/fncom.2010.001441183Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlationsDmitry R Lyamzin0Dmitry R Lyamzin1Jakob H Macke2Jakob H Macke3Nicholas A Lesica4Nicholas A Lesica5Ludwig-Maximilians-University MunichUniversity College LondonMax Planck Institute for Biological CyberneticsUniversity of TübingenLudwig-Maximilians-University MunichUniversity College LondonAs multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modeling such data must also be developed. Here, we present a model for the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate (as described by its PSTH) and the dependencies between cells are characterized by both signal and noise correlations. This model is an extension of previous attempts to model population spike trains designed to control only the total correlation between cells. In our model, the response of each cell is represented as a binary vector given by the dichotomized sum of a deterministic ‘signal’ that is repeated on each trial and a Gaussian random ‘noise’ that is different on each trial. This model allows the simulation of population spike trains with PSTHs, trial-to-trial variability, and pairwise correlations that match those measured experimentally. Furthermore, the model also allows the noise correlations in the spike trains to be manipulated independently of the signal correlations and single cell properties. To demonstrate the utility of the model, we use it to simulate and manipulate experimental responses from the mammalian auditory and visual systems. We also present a general form of the model in which both the signal and noise are Gaussian random processes, allowing the mean spike rate, trial-to-trial variability, and pairwise signal and noise correlations to be specified independently. Together, these methods for modeling spike trains comprise a potentially powerful set of tools for both theorists and experimentalists studying population responses in sensory systems.http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00144/fullPopulationsimulationCorrelationModelnoise correlation |
| spellingShingle | Dmitry R Lyamzin Dmitry R Lyamzin Jakob H Macke Jakob H Macke Nicholas A Lesica Nicholas A Lesica Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations Frontiers in Computational Neuroscience Population simulation Correlation Model noise correlation |
| title | Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations |
| title_full | Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations |
| title_fullStr | Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations |
| title_full_unstemmed | Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations |
| title_short | Modeling population spike trains with specified time-varying spike rates, trial-to-trial variability, and pairwise signal and noise correlations |
| title_sort | modeling population spike trains with specified time varying spike rates trial to trial variability and pairwise signal and noise correlations |
| topic | Population simulation Correlation Model noise correlation |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00144/full |
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