Uncovering the organization of neural circuits with Generalized Phase Locking Analysis
Autor: | Theofanis I Panagiotaropoulos, Vishal Kapoor, S Safavi, Juan F. Ramirez-Villegas, Michel Besserve, Nikos K. Logothetis |
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Rok vydání: | 2023 |
Předmět: |
Quantitative Biology::Neurons and Cognition
Ecology Computer science business.industry Univariate Pattern recognition Multielectrode array Neurophysiology Field (computer science) Cellular and Molecular Neuroscience medicine.anatomical_structure Coupling (computer programming) Computational Theory and Mathematics Modeling and Simulation medicine Biological neural network Genetics Neuron Artificial intelligence business Molecular Biology Ecology Evolution Behavior and Systematics Interpretability |
Zdroj: | PLoS Computational Biology |
ISSN: | 1553-7358 |
Popis: | Despite the considerable progress ofin vivoneural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand a mesoscopic “field” signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We developGeneralized Phase Locking Analysis(GPLA) as a dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies, thereby providing rich yet interpretable information. In particular, we show that GPLA features arebiophysically interpretablewhen used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA used jointly with biophysical modeling can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings. |
Databáze: | OpenAIRE |
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