Encoding Through Patterns: Regression Tree--Based Neuronal Population Models.
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| Title: | Encoding Through Patterns: Regression Tree--Based Neuronal Population Models. |
|---|---|
| Authors: | Haslinger, Robert, Pipa, Gordon, Lewis, Laura D., Nikolić, Danko, Williams, Ziv, Brown, Emery |
| Source: | Neural Computation. Aug2013, Vol. 25 Issue 8, p1953-1993. 41p. 10 Diagrams. |
| Subjects: | Coding theory, Regression analysis, Neuroplasticity, Probability theory, Maximum likelihood decoding, Acquisition of data |
| Abstract: | Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The challenge is that large populations may express an astronomical number of unique patterns, and so fitting a unique encoding model for each individual pattern is not feasible. We avoid this combinatorial problem using a dimensionality-reduction approach based on regression trees. Using the insight that some patterns may, from the perspective of encoding, be statistically indistinguishable, the tree divisively clusters the observed patterns into groups whose member patterns possess similar encoding properties. These groups, corresponding to the leaves of the tree, are much smaller in number than the original patterns, and the tree itself constitutes a tractable encoding model for each pattern. Our formalism can detect an extremely weak stimulus-driven pattern structure and is based on maximizing the data likelihood, not making a priori assumptions as to how patterns should be grouped. Most important, by comparing pattern encodings with independent neuron encodings, one can determine if neurons in the population are driven independently or collectively. We demonstrate this method using multiple unit recordings from area 17 of anesthetized cat in response to a sinusoidal grating and show that pattern-based encodings are superior to those of independent neuron models. The agnostic nature of our clustering approach allows us to investigate encoding by the collective statistics that are actually present rather than those (such as pairwise) that might be presumed. [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: 88318318 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Encoding Through Patterns: Regression Tree--Based Neuronal Population Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Haslinger%2C+Robert%22">Haslinger, Robert</searchLink><br /><searchLink fieldCode="AR" term="%22Pipa%2C+Gordon%22">Pipa, Gordon</searchLink><br /><searchLink fieldCode="AR" term="%22Lewis%2C+Laura+D%2E%22">Lewis, Laura D.</searchLink><br /><searchLink fieldCode="AR" term="%22Nikolić%2C+Danko%22">Nikolić, Danko</searchLink><br /><searchLink fieldCode="AR" term="%22Williams%2C+Ziv%22">Williams, Ziv</searchLink><br /><searchLink fieldCode="AR" term="%22Brown%2C+Emery%22">Brown, Emery</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Aug2013, Vol. 25 Issue 8, p1953-1993. 41p. 10 Diagrams. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Coding+theory%22">Coding theory</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Neuroplasticity%22">Neuroplasticity</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+decoding%22">Maximum likelihood decoding</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The challenge is that large populations may express an astronomical number of unique patterns, and so fitting a unique encoding model for each individual pattern is not feasible. We avoid this combinatorial problem using a dimensionality-reduction approach based on regression trees. Using the insight that some patterns may, from the perspective of encoding, be statistically indistinguishable, the tree divisively clusters the observed patterns into groups whose member patterns possess similar encoding properties. These groups, corresponding to the leaves of the tree, are much smaller in number than the original patterns, and the tree itself constitutes a tractable encoding model for each pattern. Our formalism can detect an extremely weak stimulus-driven pattern structure and is based on maximizing the data likelihood, not making a priori assumptions as to how patterns should be grouped. Most important, by comparing pattern encodings with independent neuron encodings, one can determine if neurons in the population are driven independently or collectively. We demonstrate this method using multiple unit recordings from area 17 of anesthetized cat in response to a sinusoidal grating and show that pattern-based encodings are superior to those of independent neuron models. The agnostic nature of our clustering approach allows us to investigate encoding by the collective statistics that are actually present rather than those (such as pairwise) that might be presumed. [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_00464 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 41 StartPage: 1953 Subjects: – SubjectFull: Coding theory Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Neuroplasticity Type: general – SubjectFull: Probability theory Type: general – SubjectFull: Maximum likelihood decoding Type: general – SubjectFull: Acquisition of data Type: general Titles: – TitleFull: Encoding Through Patterns: Regression Tree--Based Neuronal Population Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Haslinger, Robert – PersonEntity: Name: NameFull: Pipa, Gordon – PersonEntity: Name: NameFull: Lewis, Laura D. – PersonEntity: Name: NameFull: Nikolić, Danko – PersonEntity: Name: NameFull: Williams, Ziv – PersonEntity: Name: NameFull: Brown, Emery IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 25 – Type: issue Value: 8 Titles: – TitleFull: Neural Computation Type: main |
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