Fast Multigroup Gaussian Process Factor Models.
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| Title: | Fast Multigroup Gaussian Process Factor Models. |
|---|---|
| Authors: | Gokcen, Evren (AUTHOR), Jasper, Anna I. (AUTHOR), Kohn, Adam (AUTHOR), Machens, Christian K. (AUTHOR), Yu, Byron M. (AUTHOR) |
| Source: | Neural Computation. Sep2025, Vol. 37 Issue 9, p1709-1782. 74p. |
| Subjects: | Gaussian processes, Frequency-domain analysis, Neurosciences, Dimensional reduction algorithms, Mathematical optimization, Neurophysiology |
| Abstract: | Gaussian processes are now commonly used in dimensionality reduction approaches tailored to neuroscience, especially to describe changes in high-dimensional neural activity over time. As recording capabilities expand to include neuronal populations across multiple brain areas, cortical layers, and cell types, interest in extending gaussian process factor models to characterize multipopulation interactions has grown. However, the cubic runtime scaling of current methods with the length of experimental trials and the number of recorded populations (groups) precludes their application to large-scale multipopulation recordings. Here, we improve this scaling from cubic to linear in both trial length and group number. We present two approximate approaches to fitting multigroup gaussian process factor models based on inducing variables and the frequency domain. Empirically, both methods achieved orders of magnitude speed-up with minimal impact on statistical performance, in simulation and on neural recordings of hundreds of neurons across three brain areas. The frequency domain approach, in particular, consistently provided the greatest runtime benefits with the fewest trade-offs in statistical performance. We further characterize the estimation biases introduced by the frequency domain approach and demonstrate effective strategies to mitigate them. This work enables a powerful class of analysis techniques to keep pace with the growing scale of multipopulation recordings, opening new avenues for exploring brain function. [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 | Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 187242895 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fast Multigroup Gaussian Process Factor Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gokcen%2C+Evren%22">Gokcen, Evren</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jasper%2C+Anna+I%2E%22">Jasper, Anna I.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kohn%2C+Adam%22">Kohn, Adam</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Machens%2C+Christian+K%2E%22">Machens, Christian K.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Byron+M%2E%22">Yu, Byron M.</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Sep2025, Vol. 37 Issue 9, p1709-1782. 74p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Frequency-domain+analysis%22">Frequency-domain analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Neurophysiology%22">Neurophysiology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Gaussian processes are now commonly used in dimensionality reduction approaches tailored to neuroscience, especially to describe changes in high-dimensional neural activity over time. As recording capabilities expand to include neuronal populations across multiple brain areas, cortical layers, and cell types, interest in extending gaussian process factor models to characterize multipopulation interactions has grown. However, the cubic runtime scaling of current methods with the length of experimental trials and the number of recorded populations (groups) precludes their application to large-scale multipopulation recordings. Here, we improve this scaling from cubic to linear in both trial length and group number. We present two approximate approaches to fitting multigroup gaussian process factor models based on inducing variables and the frequency domain. Empirically, both methods achieved orders of magnitude speed-up with minimal impact on statistical performance, in simulation and on neural recordings of hundreds of neurons across three brain areas. The frequency domain approach, in particular, consistently provided the greatest runtime benefits with the fewest trade-offs in statistical performance. We further characterize the estimation biases introduced by the frequency domain approach and demonstrate effective strategies to mitigate them. This work enables a powerful class of analysis techniques to keep pace with the growing scale of multipopulation recordings, opening new avenues for exploring brain function. [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.22 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 74 StartPage: 1709 Subjects: – SubjectFull: Gaussian processes Type: general – SubjectFull: Frequency-domain analysis Type: general – SubjectFull: Neurosciences Type: general – SubjectFull: Dimensional reduction algorithms Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Neurophysiology Type: general Titles: – TitleFull: Fast Multigroup Gaussian Process Factor Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gokcen, Evren – PersonEntity: Name: NameFull: Jasper, Anna I. – PersonEntity: Name: NameFull: Kohn, Adam – PersonEntity: Name: NameFull: Machens, Christian K. – PersonEntity: Name: NameFull: Yu, Byron M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 37 – Type: issue Value: 9 Titles: – TitleFull: Neural Computation Type: main |
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