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Uncovering the organization of neural circuits with generalized phase-locking analysis

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Kapoor,  V
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Besserve,  M
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Safavi, S., Panagiotaropoulos, F., Kapoor, V., Ramirez-Villegas, J., Logothetis, N., & Besserve, M. (2020). Uncovering the organization of neural circuits with generalized phase-locking analysis. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2020), Denver, CO, USA.


Cite as: http://hdl.handle.net/21.11116/0000-0005-EC16-9
Abstract
Many neurophysiological recordings provide concurrent signals of two different nature: On the one hand, the timestamps of action potentials reflect the information sent by individual neurons, and on the other hand, Local field Potentials (LFP) reflect multiple sub-threshold and postsynaptic mechanisms related to the underlying network activity. The synchronization between spiking activity and the phase of particular LFP rhythms has been used as an important marker to reason about the underlying cooperative network mechanisms. In order to extract in a systematic way coupling information from the largely multivariate data available in current recording techniques, we study a multivariate extension of spike-field coupling analysis. After whitening band-passed LFP signals, we collect normalized pairwise complex-value spike-field coupling coefficients in a rectangular matrix and summarize its structure with the largest singular value and the corresponding singular vectors. Singular vectors represent the dominant LFP and spiking patterns and the singular value, called generalized Phase Locking Value (gPLV), characterizes the strength of the coupling between LFP and spike patterns. We further investigate the statistical properties of the gPLV and develop an empirical and theoretical statistical testing framework. We apply the method to various simulated and experimental datasets. first, GPLA’s performance is superior to univariate measures in the presence of large amounts of noise. Next, application of GPLA on simulations of hippocampal sharp-waveripples (SWR) reveals various characteristics of hippocampal circuitry (e.g. communication flow from CA3 to CA1 during the SWR) with minimal prior knowledge. Furthermore, application of GPLA on Utah array recordings in anesthetized macaque suggests a non-trivial coupling between spiking activity and LFP traveling wave in the ventrolateral Prefrontal Cortex (vlPFC). In summary, with GPLA, we can quantify, characterize and statistically assess the interactions between population spiking activity and mesoscopic network dynamics.