Statistics of neuronal identification with open and closed loop measures of intrinsic excitability
In complex nervous systems patterns of neuronal activity and measures of intrinsic neuronal excitability are often used as criteria for identifying and/or classifying neurons. We asked how well identification of neurons by conventional measures of intrinsic excitability compares with a measure of n...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-04-01
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| Series: | Frontiers in Neural Circuits |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncir.2012.00019/full |
| _version_ | 1818980889724452864 |
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| author | Ted eBrookings Rachel eGrashow Eve eMarder |
| author_facet | Ted eBrookings Rachel eGrashow Eve eMarder |
| author_sort | Ted eBrookings |
| collection | DOAJ |
| description | In complex nervous systems patterns of neuronal activity and measures of intrinsic neuronal excitability are often used as criteria for identifying and/or classifying neurons. We asked how well identification of neurons by conventional measures of intrinsic excitability compares with a measure of neuronal excitability derived from a neuron’s behavior in a dynamic clamp constructed two cell network. We used four cell types from the crab stomatogastric ganglion, the Pyloric Dilator (PD), Lateral Pyloric (LP), Gastric Mill (GM), and Dorsal Gastric (DG) neurons. Each neuron was evaluated for six conventional measures of intrinsic excitability (Intrinsic Properties; IPs). Additionally, each neuron was coupled by reciprocal inhibitory synapses made with the dynamic clamp to a Morris-Lecar model neuron and the resulting network was assayed for four measures of network activity (network activity properties; NAPs). We searched for linear combinations of IPs that correlated with each NAP, and combinations of NAPs that correlated with each IP. In the process we developed a method to correct for multiple correlations while searching for correlating features. When properly controlled for multiple correlations, four of the IPs were correlated with NAPs, and all four NAPs were correlated with IPs. Neurons were classified into cell types by training a linear classifier on sets of properties, or using k-medoids clustering. The IPs were modestly successful in classifying the neurons, and the NAPs were more successful. Combining the two measures did better than either measure alone, but not well enough to classify neurons with perfect accuracy, thus reiterating the need to combine electrophysiology with another, independent criterion for cell identification. |
| first_indexed | 2024-12-20T17:22:36Z |
| format | Article |
| id | doaj.art-83f32469af04468095907eb8e859077f |
| institution | Directory Open Access Journal |
| issn | 1662-5110 |
| language | English |
| last_indexed | 2024-12-20T17:22:36Z |
| publishDate | 2012-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neural Circuits |
| spelling | doaj.art-83f32469af04468095907eb8e859077f2022-12-21T19:31:43ZengFrontiers Media S.A.Frontiers in Neural Circuits1662-51102012-04-01610.3389/fncir.2012.0001923018Statistics of neuronal identification with open and closed loop measures of intrinsic excitabilityTed eBrookings0Rachel eGrashow1Eve eMarder2Brandeis UniversityHarvard School of Public Health Harvard School of Public HealthBrandeis UniversityIn complex nervous systems patterns of neuronal activity and measures of intrinsic neuronal excitability are often used as criteria for identifying and/or classifying neurons. We asked how well identification of neurons by conventional measures of intrinsic excitability compares with a measure of neuronal excitability derived from a neuron’s behavior in a dynamic clamp constructed two cell network. We used four cell types from the crab stomatogastric ganglion, the Pyloric Dilator (PD), Lateral Pyloric (LP), Gastric Mill (GM), and Dorsal Gastric (DG) neurons. Each neuron was evaluated for six conventional measures of intrinsic excitability (Intrinsic Properties; IPs). Additionally, each neuron was coupled by reciprocal inhibitory synapses made with the dynamic clamp to a Morris-Lecar model neuron and the resulting network was assayed for four measures of network activity (network activity properties; NAPs). We searched for linear combinations of IPs that correlated with each NAP, and combinations of NAPs that correlated with each IP. In the process we developed a method to correct for multiple correlations while searching for correlating features. When properly controlled for multiple correlations, four of the IPs were correlated with NAPs, and all four NAPs were correlated with IPs. Neurons were classified into cell types by training a linear classifier on sets of properties, or using k-medoids clustering. The IPs were modestly successful in classifying the neurons, and the NAPs were more successful. Combining the two measures did better than either measure alone, but not well enough to classify neurons with perfect accuracy, thus reiterating the need to combine electrophysiology with another, independent criterion for cell identification.http://journal.frontiersin.org/Journal/10.3389/fncir.2012.00019/fulldynamic clampclustering algorithmsFeature Selectionhalf-center oscillatoridentified neuronsMorris-Lecar model |
| spellingShingle | Ted eBrookings Rachel eGrashow Eve eMarder Statistics of neuronal identification with open and closed loop measures of intrinsic excitability Frontiers in Neural Circuits dynamic clamp clustering algorithms Feature Selection half-center oscillator identified neurons Morris-Lecar model |
| title | Statistics of neuronal identification with open and closed loop measures of intrinsic excitability |
| title_full | Statistics of neuronal identification with open and closed loop measures of intrinsic excitability |
| title_fullStr | Statistics of neuronal identification with open and closed loop measures of intrinsic excitability |
| title_full_unstemmed | Statistics of neuronal identification with open and closed loop measures of intrinsic excitability |
| title_short | Statistics of neuronal identification with open and closed loop measures of intrinsic excitability |
| title_sort | statistics of neuronal identification with open and closed loop measures of intrinsic excitability |
| topic | dynamic clamp clustering algorithms Feature Selection half-center oscillator identified neurons Morris-Lecar model |
| url | http://journal.frontiersin.org/Journal/10.3389/fncir.2012.00019/full |
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