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Geometry of spiking patterns in early visual cortex : a topological data analytic approach
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- Guidolin, Andrea (author)
- KTH,SFW Matematik för Data och AI,Basque Ctr Appl Math, MCEN Team, BCAM, Bilbao 48009, Basque, Spain.
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- Desroches, Mathieu (author)
- Inria Univ Cote Azur, MathNeuro Team, F-06902 Sophia Antipolis, France.
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- Victor, Jonathan D. D. (author)
- Feil Family Brain & Mind Res Inst, Weill Cornell Med Coll, New York, NY 10065 USA.
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- (creator_code:org_t)
- 2022-11-16
- 2022
- English.
- In: Journal of the Royal Society Interface. - : The Royal Society. - 1742-5689 .- 1742-5662. ; 19:196
- Journal article (peer-reviewed)
Abstract
Subject headings
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- In the brain, spiking patterns live in a high-dimensional space of neurons and time. Thus, determining the intrinsic structure of this space presents a theoretical and experimental challenge. To address this challenge, we introduce a new framework for applying topological data analysis (TDA) to spike train data and use it to determine the geometry of spiking patterns in the visual cortex. Key to our approach is a parametrized family of distances based on the timing of spikes that quantifies the dissimilarity between neuronal responses. We applied TDA to visually driven single-unit and multiple single-unit spiking activity in macaque V1 and V2. TDA across timescales reveals a common geometry for spiking patterns in V1 and V2 which, among simple models, is most similar to that of a low-dimensional space endowed with Euclidean or hyperbolic geometry with modest curvature. Remarkably, the inferred geometry depends on timescale and is clearest for the timescales that are important for encoding contrast, orientation and spatial correlations.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
Keyword
- topological data analysis
- persistent homology
- spike metric
- visual cortex
Publication and Content Type
- ref (subject category)
- art (subject category)
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