Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings
One of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-08-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fninf.2017.00053/full |
| _version_ | 1828472833924136960 |
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| author | Yasamin Mokri Rodrigo F. Salazar Baldwin Goodell Jonathan Baker Charles M. Gray Shih-Cheng Yen |
| author_facet | Yasamin Mokri Rodrigo F. Salazar Baldwin Goodell Jonathan Baker Charles M. Gray Shih-Cheng Yen |
| author_sort | Yasamin Mokri |
| collection | DOAJ |
| description | One of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that is capable of resolving overlapping spikes. After constructing template waveforms for well-isolated and distinct single units, we generated pair-wise combinations of those templates at all possible time shifts from each other. Subsequently, overlapping waveforms were identified by cluster analysis, and then assigned to their respective single-unit combinations. We examined the performance of this method using simulated data from an earlier study, and found that we were able to resolve an average of 83% of the overlapping waveforms across various signal-to-noise ratios, an improvement of approximately 32% over the results reported in the earlier study. When applied to additional simulated data sets generated from single-electrode and tetrode recordings, we were able to resolve 91% of the overlapping waveforms with a false positive rate of 0.19% for single-electrode data, and 95% of the overlapping waveforms with a false positive rate of 0.27% for tetrode data. We also applied our method to electrode and tetrode data recorded from the primary visual cortex, and the results obtained for these datasets suggest that our method provides an efficient means of sorting overlapping waveforms. This method can easily be added as an extra step to commonly used spike sorting methods, such as KlustaKwik and MClust software packages, and can be applied to datasets that have already been sorted using these methods. |
| first_indexed | 2024-12-11T05:35:34Z |
| format | Article |
| id | doaj.art-9e164a0115034b47ac092c493c2ed736 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-11T05:35:34Z |
| publishDate | 2017-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-9e164a0115034b47ac092c493c2ed7362022-12-22T01:19:18ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962017-08-011110.3389/fninf.2017.00053272755Sorting Overlapping Spike Waveforms from Electrode and Tetrode RecordingsYasamin Mokri0Rodrigo F. Salazar1Baldwin Goodell2Jonathan Baker3Charles M. Gray4Shih-Cheng Yen5Department of Electrical and Computer Engineering, National University of SingaporeSingapore, SingaporeDepartment of Cell Biology and Neuroscience, Montana State University, BozemanMT, United StatesDepartment of Cell Biology and Neuroscience, Montana State University, BozemanMT, United StatesDepartment of Cell Biology and Neuroscience, Montana State University, BozemanMT, United StatesDepartment of Cell Biology and Neuroscience, Montana State University, BozemanMT, United StatesDepartment of Electrical and Computer Engineering, National University of SingaporeSingapore, SingaporeOne of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that is capable of resolving overlapping spikes. After constructing template waveforms for well-isolated and distinct single units, we generated pair-wise combinations of those templates at all possible time shifts from each other. Subsequently, overlapping waveforms were identified by cluster analysis, and then assigned to their respective single-unit combinations. We examined the performance of this method using simulated data from an earlier study, and found that we were able to resolve an average of 83% of the overlapping waveforms across various signal-to-noise ratios, an improvement of approximately 32% over the results reported in the earlier study. When applied to additional simulated data sets generated from single-electrode and tetrode recordings, we were able to resolve 91% of the overlapping waveforms with a false positive rate of 0.19% for single-electrode data, and 95% of the overlapping waveforms with a false positive rate of 0.27% for tetrode data. We also applied our method to electrode and tetrode data recorded from the primary visual cortex, and the results obtained for these datasets suggest that our method provides an efficient means of sorting overlapping waveforms. This method can easily be added as an extra step to commonly used spike sorting methods, such as KlustaKwik and MClust software packages, and can be applied to datasets that have already been sorted using these methods.http://journal.frontiersin.org/article/10.3389/fninf.2017.00053/fullspike sortingoverlapping waveformstetrodevisual cortexelectrophysiology |
| spellingShingle | Yasamin Mokri Rodrigo F. Salazar Baldwin Goodell Jonathan Baker Charles M. Gray Shih-Cheng Yen Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings Frontiers in Neuroinformatics spike sorting overlapping waveforms tetrode visual cortex electrophysiology |
| title | Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings |
| title_full | Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings |
| title_fullStr | Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings |
| title_full_unstemmed | Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings |
| title_short | Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings |
| title_sort | sorting overlapping spike waveforms from electrode and tetrode recordings |
| topic | spike sorting overlapping waveforms tetrode visual cortex electrophysiology |
| url | http://journal.frontiersin.org/article/10.3389/fninf.2017.00053/full |
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