Simulation Based Inference of a Simple Neural Network Structure

TL;DR

This paper introduces a simulation-based method to infer neural network structure from spike train statistics, outperforming traditional correlation-based approaches on a toy model.

Contribution

The authors propose a novel simulation-based inference approach using simple spike train statistics, improving network structure estimation from limited data.

Findings

Simulation-based method outperforms correlation methods in a toy model

Spike train statistics can reliably infer underlying network structure

Method shows significant improvement in connection probability estimation

Abstract

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 that constitutes the network. Using association statistics such as the estimation of the cross-correlation functions, they are trying to 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. We propose to focus instead on simple spike train statistics, like the empirical spikes frequency, or the interspike interval distribution. Their sampling distributions can be estimated by simulations, and, given a few observed spike train statistics, they provide enough information to infer the structure of the underlying network. We show that, on a ''toy model'', our method gives significantly better results than the sub-network reconstruction method with regards to the inference of the connection probability of the original network.