Interneuron Types as Attractors and Controllers.
Saved in:
| Title: | Interneuron Types as Attractors and Controllers. |
|---|---|
| Authors: | Fishell, Gord (AUTHOR), Kepecs, Adam (AUTHOR) |
| Source: | Annual Review of Neuroscience. Jul2020, Vol. 43, p1-30. 23p. |
| Subjects: | Gene regulatory networks, Transcription factors, Division of labor, Circuit complexity |
| Abstract: | Cortical interneurons display striking differences in shape, physiology, and other attributes, challenging us to appropriately classify them. We previously suggested that interneuron types should be defined by their role in cortical processing. Here, we revisit the question of how to codify their diversity based upon their division of labor and function as controllers of cortical information flow. We suggest that developmental trajectories provide a guide for appreciating interneuron diversity and argue that subtype identity is generated using a configurational (rather than combinatorial) code of transcription factors that produce attractor states in the underlying gene regulatory network. We present our updated three-stage model for interneuron specification: an initial cardinal step, allocating interneurons into a few major classes, followed by definitive refinement, creating subclasses upon settling within the cortex, and lastly, state determination, reflecting the incorporation of interneurons into functional circuit ensembles. We close by discussing findings indicating that major interneuron classes are both evolutionarily ancient and conserved. We propose that the complexity of cortical circuits is generated by phylogenetically old interneuron types, complemented by an evolutionary increase in principal neuron diversity. This suggests that a natural neurobiological definition of interneuron types might be derived from a match between their developmental origin and computational function. [ABSTRACT FROM AUTHOR] |
| Copyright of Annual Review of Neuroscience is the property of Annual Reviews Inc. 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 | Text: Availability: 0 |
|---|---|
| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 144454855 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Interneuron Types as Attractors and Controllers. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fishell%2C+Gord%22">Fishell, Gord</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kepecs%2C+Adam%22">Kepecs, Adam</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Annual+Review+of+Neuroscience%22">Annual Review of Neuroscience</searchLink>. Jul2020, Vol. 43, p1-30. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gene+regulatory+networks%22">Gene regulatory networks</searchLink><br /><searchLink fieldCode="DE" term="%22Transcription+factors%22">Transcription factors</searchLink><br /><searchLink fieldCode="DE" term="%22Division+of+labor%22">Division of labor</searchLink><br /><searchLink fieldCode="DE" term="%22Circuit+complexity%22">Circuit complexity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cortical interneurons display striking differences in shape, physiology, and other attributes, challenging us to appropriately classify them. We previously suggested that interneuron types should be defined by their role in cortical processing. Here, we revisit the question of how to codify their diversity based upon their division of labor and function as controllers of cortical information flow. We suggest that developmental trajectories provide a guide for appreciating interneuron diversity and argue that subtype identity is generated using a configurational (rather than combinatorial) code of transcription factors that produce attractor states in the underlying gene regulatory network. We present our updated three-stage model for interneuron specification: an initial cardinal step, allocating interneurons into a few major classes, followed by definitive refinement, creating subclasses upon settling within the cortex, and lastly, state determination, reflecting the incorporation of interneurons into functional circuit ensembles. We close by discussing findings indicating that major interneuron classes are both evolutionarily ancient and conserved. We propose that the complexity of cortical circuits is generated by phylogenetically old interneuron types, complemented by an evolutionary increase in principal neuron diversity. This suggests that a natural neurobiological definition of interneuron types might be derived from a match between their developmental origin and computational function. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Annual Review of Neuroscience is the property of Annual Reviews Inc. 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=144454855 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1146/annurev-neuro-070918-050421 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1 Subjects: – SubjectFull: Gene regulatory networks Type: general – SubjectFull: Transcription factors Type: general – SubjectFull: Division of labor Type: general – SubjectFull: Circuit complexity Type: general Titles: – TitleFull: Interneuron Types as Attractors and Controllers. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fishell, Gord – PersonEntity: Name: NameFull: Kepecs, Adam IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 0147006X Numbering: – Type: volume Value: 43 Titles: – TitleFull: Annual Review of Neuroscience Type: main |
| ResultId | 1 |
