Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks.
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| Title: | Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks. |
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| Authors: | Frady, E. Paxon, Kapoor, Ashish, Horvitz, Eric, Kristan Jr, William B. |
| Source: | Neural Computation. 2016, Vol. 28 Issue 8, p1453-1497. 45p. 1 Diagram, 2 Charts, 9 Graphs. |
| Subjects: | Neurons, Cartography, Acquisition of data, Intersubjectivity, Behavior evolution, Voltage-sensitive dyes, Anatomical specimens |
| Abstract: | Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/ subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography--the large-scale mapping of neural activity during behavioral states. Using a voltage-sensitive dye (VSD), we imaged the multifunctional responses of hundreds of leech neurons during several behaviors to identify and functionally map homologous neurons. We extracted simple features from each of these behaviors and combined them with anatomical features to create a rich medium-dimensional feature space. This enabled us to use machine learning techniques and visualizations to characterize and account for intersubject variability, piece together a canonical atlas of neural activity, and identify two behavioral networks. We identified 39 neurons (18 pairs, 3 unpaired) as part of a canonical swim network and 17 neurons (8 pairs, 1 unpaired) involved in a partially overlapping preparatory network. All neurons in the preparatory network rapidly depolarized at the onsets of each behavior, suggesting that it is part of a dedicated rapidresponse network. This network is likely mediated by the S cell, and we referencedVSDrecordings to an activity atlas to identify multiple cells of interest simultaneously in real time for further experiments. We targeted and electrophysiologically verified several neurons in the swim network and further showed that the S cell is presynaptic to multiple neurons in the preparatory network. This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform analyses in light of intersubject variability. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 117047809 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Frady%2C+E%2E+Paxon%22">Frady, E. Paxon</searchLink><br /><searchLink fieldCode="AR" term="%22Kapoor%2C+Ashish%22">Kapoor, Ashish</searchLink><br /><searchLink fieldCode="AR" term="%22Horvitz%2C+Eric%22">Horvitz, Eric</searchLink><br /><searchLink fieldCode="AR" term="%22Kristan+Jr%2C+William+B%2E%22">Kristan Jr, William B.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. 2016, Vol. 28 Issue 8, p1453-1497. 45p. 1 Diagram, 2 Charts, 9 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Neurons%22">Neurons</searchLink><br /><searchLink fieldCode="DE" term="%22Cartography%22">Cartography</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Intersubjectivity%22">Intersubjectivity</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+evolution%22">Behavior evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Voltage-sensitive+dyes%22">Voltage-sensitive dyes</searchLink><br /><searchLink fieldCode="DE" term="%22Anatomical+specimens%22">Anatomical specimens</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/ subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography--the large-scale mapping of neural activity during behavioral states. Using a voltage-sensitive dye (VSD), we imaged the multifunctional responses of hundreds of leech neurons during several behaviors to identify and functionally map homologous neurons. We extracted simple features from each of these behaviors and combined them with anatomical features to create a rich medium-dimensional feature space. This enabled us to use machine learning techniques and visualizations to characterize and account for intersubject variability, piece together a canonical atlas of neural activity, and identify two behavioral networks. We identified 39 neurons (18 pairs, 3 unpaired) as part of a canonical swim network and 17 neurons (8 pairs, 1 unpaired) involved in a partially overlapping preparatory network. All neurons in the preparatory network rapidly depolarized at the onsets of each behavior, suggesting that it is part of a dedicated rapidresponse network. This network is likely mediated by the S cell, and we referencedVSDrecordings to an activity atlas to identify multiple cells of interest simultaneously in real time for further experiments. We targeted and electrophysiologically verified several neurons in the swim network and further showed that the S cell is presynaptic to multiple neurons in the preparatory network. This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform analyses in light of intersubject variability. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/NECO_a_00852 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 45 StartPage: 1453 Subjects: – SubjectFull: Neurons Type: general – SubjectFull: Cartography Type: general – SubjectFull: Acquisition of data Type: general – SubjectFull: Intersubjectivity Type: general – SubjectFull: Behavior evolution Type: general – SubjectFull: Voltage-sensitive dyes Type: general – SubjectFull: Anatomical specimens Type: general Titles: – TitleFull: Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Frady, E. Paxon – PersonEntity: Name: NameFull: Kapoor, Ashish – PersonEntity: Name: NameFull: Horvitz, Eric – PersonEntity: Name: NameFull: Kristan Jr, William B. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: 2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 28 – Type: issue Value: 8 Titles: – TitleFull: Neural Computation Type: main |
| ResultId | 1 |
