Discovering optimal features for neuron-type identification from extracellular recordings
Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types ofte...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2024.1303993/full |
| _version_ | 1797334801494048768 |
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| author | Vergil R. Haynes Vergil R. Haynes Yi Zhou Sharon M. Crook |
| author_facet | Vergil R. Haynes Vergil R. Haynes Yi Zhou Sharon M. Crook |
| author_sort | Vergil R. Haynes |
| collection | DOAJ |
| description | Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification. |
| first_indexed | 2024-03-08T08:27:27Z |
| format | Article |
| id | doaj.art-6d77e631c79e42f98232d5c4a3cdaaaf |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-03-08T08:27:27Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-6d77e631c79e42f98232d5c4a3cdaaaf2024-02-02T04:29:15ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962024-02-011810.3389/fninf.2024.13039931303993Discovering optimal features for neuron-type identification from extracellular recordingsVergil R. Haynes0Vergil R. Haynes1Yi Zhou2Sharon M. Crook3Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United StatesLaboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United StatesLaboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United StatesLaboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United StatesAdvancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.https://www.frontiersin.org/articles/10.3389/fninf.2024.1303993/fullextracellular action potentialsmachine learningneuron-type predictionsimulated EAPneuron models |
| spellingShingle | Vergil R. Haynes Vergil R. Haynes Yi Zhou Sharon M. Crook Discovering optimal features for neuron-type identification from extracellular recordings Frontiers in Neuroinformatics extracellular action potentials machine learning neuron-type prediction simulated EAP neuron models |
| title | Discovering optimal features for neuron-type identification from extracellular recordings |
| title_full | Discovering optimal features for neuron-type identification from extracellular recordings |
| title_fullStr | Discovering optimal features for neuron-type identification from extracellular recordings |
| title_full_unstemmed | Discovering optimal features for neuron-type identification from extracellular recordings |
| title_short | Discovering optimal features for neuron-type identification from extracellular recordings |
| title_sort | discovering optimal features for neuron type identification from extracellular recordings |
| topic | extracellular action potentials machine learning neuron-type prediction simulated EAP neuron models |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2024.1303993/full |
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