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Article Dans Une Revue Journal of Neuroscience Methods Année : 2022

Neuronal Network Inference and Membrane Potential Model using Multivariate Hawkes Processes

Résumé

In this work, we propose to catch the complexity of the membrane potential’s dynamic of a motoneuron between its spikes, taking into account the spikes from other neurons around. Our approach relies on two types of data: extracellular recordings of multiple spikes trains and intracellular recordings of the membrane potential of a central neuron. Our main contri- bution is to provide a unified framework and a complete pipeline to analyze neuronal activity from data extraction to statistical inference. The first step of the procedure is to select a subnetwork of neurons impacting the central neuron: we use a multivariate Hawkes process to model the spike trains of all neurons and compare two sparse inference procedures to identify the connectivity graph. Then we infer a jump-diffusion dynamic in which jumps are driven from a Hawkes process, the occurrences of which correspond to the spike trains of the aforementioned subset of neurons that interact with the central neuron. We validate the Hawkes model with a goodness-of-fit test and we show that taking into account the informa- tion from the connectivity graph improves the inference of the jump-diffusion process. The entire code has been developed and is freely available on GitHub.
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Dates et versions

hal-03309709 , version 1 (30-07-2021)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

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Anna Bonnet, Charlotte Dion, François Gindraud, Sarah Lemler. Neuronal Network Inference and Membrane Potential Model using Multivariate Hawkes Processes. Journal of Neuroscience Methods, 2022, 372, pp.109550. ⟨10.1016/j.jneumeth.2022.109550⟩. ⟨hal-03309709⟩
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