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Neuronal network inference and membrane potential model using multivariate Hawkes processes.

Anna Bonnet1, Charlotte Dion-Blanc1, François Gindraud2

  • 1Sorbonne Université, UMR CNRS 8001, LPSM, 75005 Paris, France.

Journal of Neuroscience Methods
|March 5, 2022
PubMed
Summary

This study introduces a novel Hawkes-diffusion model to analyze motoneuron membrane potential dynamics, integrating extracellular spike trains and intracellular recordings. The model accurately captures neuronal interactions and improves potential inference, offering a new tool for neuroscience research.

Keywords:
Diffusion processHawkes processMembrane potentialSpike trains

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Data Science in Biology

Background:

  • Neuronal communication complexity requires advanced modeling techniques.
  • Understanding motoneuron membrane potential dynamics is crucial for neuroscience.
  • Existing models often struggle to incorporate network effects from surrounding neurons.

Purpose of the Study:

  • To develop a unified framework for analyzing neuronal activity using extracellular and intracellular recordings.
  • To investigate a Hawkes-diffusion model for capturing motoneuron membrane potential dynamics influenced by network activity.
  • To improve the inference of membrane potential by accounting for spike trains from interacting neurons.

Main Methods:

  • A multivariate Hawkes process is used to identify influential neurons and their spike trains.
  • A jump-diffusion model is inferred, with jumps driven by a Hawkes process representing neuronal spikes.
  • Connectivity graphs are reconstructed using Hawkes-based sparse estimation methods.
  • The Hawkes-diffusion model's performance is compared against simple diffusion models.

Main Results:

  • A small, relevant connectivity graph impacting the central neuron is identified.
  • Incorporating network information via the Hawkes process significantly improves membrane potential inference.
  • Goodness-of-fit tests validate the effectiveness of the Hawkes model in this complex biological context.

Conclusions:

  • The proposed Hawkes-diffusion model effectively integrates spike trains and membrane potential for a comprehensive understanding of individual neuron behavior.
  • This approach offers a significant advancement in modeling neuronal dynamics within a network.
  • The developed computational pipeline and code are publicly available on GitHub.