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Mixed vine copulas as joint models of spike counts and local field potentials
Arno Onken · Stefano Panzeri

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #87 #None

Concurrent measurements of neural activity at multiple scales, sometimes performed with multimodal techniques, become increasingly important for studying brain function. However, statistical methods for their concurrent analysis are currently lacking. Here we introduce such techniques in a framework based on vine copulas with mixed margins to construct multivariate stochastic models. These models can describe detailed mixed interactions between discrete variables such as neural spike counts, and continuous variables such as local field potentials. We propose efficient methods for likelihood calculation, inference, sampling and mutual information estimation within this framework. We test our methods on simulated data and demonstrate applicability on mixed data generated by a biologically realistic neural network. Our methods hold the promise to considerably improve statistical analysis of neural data recorded simultaneously at different scales.

Author Information

Arno Onken (IIT)
Stefano Panzeri (IIT)

Stefano Panzeri studied Theoretical Physics at the University of Turin and Computational Neuroscience at SISSA, Italy. He held independent Research Fellowships and Faculty jobs at the Universities of Oxford, Newcastle, Manchester and Glasgow, and was Visiting Scholar at the Max Planck Institute for Biological Cybernetics and at Harvard Medical School. He currently works as Senior Scientist at the Italian Institute of Technology, where he directs the Laboratory of Neural Computation. His research investigates how circuits of neurons encode and transmit information.

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