Parametric models to relate spike train and LFP dynamics with neural information processing
Spike trains and local field potentials resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-07-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00051/full |
| _version_ | 1828963003744124928 |
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| author | Arpan eBanerjee Heather L Dean Bijan ePesaran |
| author_facet | Arpan eBanerjee Heather L Dean Bijan ePesaran |
| author_sort | Arpan eBanerjee |
| collection | DOAJ |
| description | Spike trains and local field potentials resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Trial-by-trial spike-field correlation in visual response onset times are higher when the unified model is used, matching with corresponding values obtained using earlier trial-averaged measures on a previously published data set. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. |
| first_indexed | 2024-12-14T10:16:39Z |
| format | Article |
| id | doaj.art-cad5f75eaae445ca8288191cba8b5262 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-14T10:16:39Z |
| publishDate | 2012-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-cad5f75eaae445ca8288191cba8b52622022-12-21T23:06:48ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882012-07-01610.3389/fncom.2012.0005126690Parametric models to relate spike train and LFP dynamics with neural information processingArpan eBanerjee0Heather L Dean1Bijan ePesaran2New York UniversityNew York UniversityNew York UniversitySpike trains and local field potentials resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Trial-by-trial spike-field correlation in visual response onset times are higher when the unified model is used, matching with corresponding values obtained using earlier trial-averaged measures on a previously published data set. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior.http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00051/fullDecodingtimingInformation ProcessinglikelihoodSpikeLFP |
| spellingShingle | Arpan eBanerjee Heather L Dean Bijan ePesaran Parametric models to relate spike train and LFP dynamics with neural information processing Frontiers in Computational Neuroscience Decoding timing Information Processing likelihood Spike LFP |
| title | Parametric models to relate spike train and LFP dynamics with neural information processing |
| title_full | Parametric models to relate spike train and LFP dynamics with neural information processing |
| title_fullStr | Parametric models to relate spike train and LFP dynamics with neural information processing |
| title_full_unstemmed | Parametric models to relate spike train and LFP dynamics with neural information processing |
| title_short | Parametric models to relate spike train and LFP dynamics with neural information processing |
| title_sort | parametric models to relate spike train and lfp dynamics with neural information processing |
| topic | Decoding timing Information Processing likelihood Spike LFP |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00051/full |
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