Simulation Based Network Structure Inference Constrained by Observed Spike Trains: SIMBADNESTICOST ANR-22-CE45-0027

1. What is it about?

Neurophysiologists are nowadays able to record from a large number of extracellular electrodes and to extract, from the raw data, the sequences of action potentials or spikes generated by many neurons. Unfortunately these "many neurons" still represent only a tiny fraction of the neuronal population which constitutes the network. Using association statistics such as the estimation of the cross-correlation functions, they try and infer the structure of the network formed by the recorded neurons. But this inference is compromised by the tremendous under-sampling of the neuronal population and by the errors made during the sequences reconstruction. This yields a "network picture" usually called a "functional network" whose features depend strongly on the recording conditions (such as the presence/absence of a stimulation). We consider that reconstructing the network formed by the recorded neurons is an ill-posed problem. We propose to focus instead on the "generative probability distribution" of the network: what is the probability to have a connection from a type A neuron to a type B neuron? Is the probability to have a connection from neuron Y of type B to neuron X of type A dependent on the presence of a connection from X to Y? We propose to simulate first the whole network using a simplified neuronal dynamics and different (parametrized) generative probability distributions. We will then compare the association statistics between the simulated and the experimentally observed cases. This type of approach is now commonly used in several fields under different names like "Approximate Bayesian Computation" or "Simulation based Inference". We will then be able to asses if there is an "over representation of reciprocal connections" using data from the first olfactory relay of an insect.

This Project runs from January 2023 to December 2026 and is funded by the ANR (ANR-22-CE45-0027) in the program AAPG2022.

2. Who is involved?

A team from the Institut de Recherche Mathématique Avancée (IRMA) in the University of Strasbourg:

3. Key ideas

We are going to give full details on how we implement the project as publications proceed, but the key ideas were already plainly stated in:

4. Publications

  1. Antonio Galves, Eva Löcherbach and Christophe Pouzat (2024) Probabilistic Spiking Neuronal Nets. Neuromathematics for the Computer Era, Springer, book series: Lecture Notes on Mathematical Modelling in the Life Sciences. The companion website describes and documents the codes simulating the models introduced in the book.
  2. Morgan André, Christophe Pouzat (2025) A Quasi-Stationary Approach to Metastability in a System of Spiking Neurons with Synaptic Plasticity, Mathematical Neuroscience and Applications 4, DOI: 10.46298/mna.7668; the associated simulation and analysis codes are available from the following GitLab repository: Metastability in a System of Spiking Neurons with Synaptic Plasticity.

5. Talks

Author: Christophe Pouzat

Created: 2025-01-14 mar. 16:45

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