Mixed vine copula flows for flexible modeling of neural dependencies
Recordings of complex neural population responses provide a unique opportunity for advancing our understanding of neural information processing at multiple scales and improving performance of brain computer interfaces. However, most existing analytical techniques fall short of capturing the complexi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.910122/full |
| _version_ | 1818058155123277824 |
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| author | Lazaros Mitskopoulos Theoklitos Amvrosiadis Arno Onken |
| author_facet | Lazaros Mitskopoulos Theoklitos Amvrosiadis Arno Onken |
| author_sort | Lazaros Mitskopoulos |
| collection | DOAJ |
| description | Recordings of complex neural population responses provide a unique opportunity for advancing our understanding of neural information processing at multiple scales and improving performance of brain computer interfaces. However, most existing analytical techniques fall short of capturing the complexity of interactions within the concerted population activity. Vine copula-based approaches have shown to be successful at addressing complex high-order dependencies within the population, disentangled from the single-neuron statistics. However, most applications have focused on parametric copulas which bear the risk of misspecifying dependence structures. In order to avoid this risk, we adopted a fully non-parametric approach for the single-neuron margins and copulas by using Neural Spline Flows (NSF). We validated the NSF framework on simulated data of continuous and discrete types with various forms of dependency structures and with different dimensionality. Overall, NSFs performed similarly to existing non-parametric estimators, while allowing for considerably faster and more flexible sampling which also enables faster Monte Carlo estimation of copula entropy. Moreover, our framework was able to capture low and higher order heavy tail dependencies in neuronal responses recorded in the mouse primary visual cortex during a visual learning task while the animal was navigating a virtual reality environment. These findings highlight an often ignored aspect of complexity in coordinated neuronal activity which can be important for understanding and deciphering collective neural dynamics for neurotechnological applications. |
| first_indexed | 2024-12-10T12:56:08Z |
| format | Article |
| id | doaj.art-de29cd6f0f3e470e8c6ca20fa8b273fb |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T12:56:08Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-de29cd6f0f3e470e8c6ca20fa8b273fb2022-12-22T01:48:06ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-09-011610.3389/fnins.2022.910122910122Mixed vine copula flows for flexible modeling of neural dependenciesLazaros Mitskopoulos0Theoklitos Amvrosiadis1Arno Onken2School of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United KingdomCentre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, United KingdomSchool of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United KingdomRecordings of complex neural population responses provide a unique opportunity for advancing our understanding of neural information processing at multiple scales and improving performance of brain computer interfaces. However, most existing analytical techniques fall short of capturing the complexity of interactions within the concerted population activity. Vine copula-based approaches have shown to be successful at addressing complex high-order dependencies within the population, disentangled from the single-neuron statistics. However, most applications have focused on parametric copulas which bear the risk of misspecifying dependence structures. In order to avoid this risk, we adopted a fully non-parametric approach for the single-neuron margins and copulas by using Neural Spline Flows (NSF). We validated the NSF framework on simulated data of continuous and discrete types with various forms of dependency structures and with different dimensionality. Overall, NSFs performed similarly to existing non-parametric estimators, while allowing for considerably faster and more flexible sampling which also enables faster Monte Carlo estimation of copula entropy. Moreover, our framework was able to capture low and higher order heavy tail dependencies in neuronal responses recorded in the mouse primary visual cortex during a visual learning task while the animal was navigating a virtual reality environment. These findings highlight an often ignored aspect of complexity in coordinated neuronal activity which can be important for understanding and deciphering collective neural dynamics for neurotechnological applications.https://www.frontiersin.org/articles/10.3389/fnins.2022.910122/fullneural dependencieshigher-order dependenciesheavy tail dependenciesvine copula flowsNeural Spline Flowsmixed variables |
| spellingShingle | Lazaros Mitskopoulos Theoklitos Amvrosiadis Arno Onken Mixed vine copula flows for flexible modeling of neural dependencies Frontiers in Neuroscience neural dependencies higher-order dependencies heavy tail dependencies vine copula flows Neural Spline Flows mixed variables |
| title | Mixed vine copula flows for flexible modeling of neural dependencies |
| title_full | Mixed vine copula flows for flexible modeling of neural dependencies |
| title_fullStr | Mixed vine copula flows for flexible modeling of neural dependencies |
| title_full_unstemmed | Mixed vine copula flows for flexible modeling of neural dependencies |
| title_short | Mixed vine copula flows for flexible modeling of neural dependencies |
| title_sort | mixed vine copula flows for flexible modeling of neural dependencies |
| topic | neural dependencies higher-order dependencies heavy tail dependencies vine copula flows Neural Spline Flows mixed variables |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.910122/full |
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