Fast Inference of Interactions in Assemblies of Stochastic Integrate-and-Fire Neurons from Spike Recordings

TL;DR

This paper introduces two Bayesian methods for inferring neuronal interactions and external currents from spike recordings, capable of handling different noise levels, validated on synthetic data and real retinal recordings.

Contribution

The paper presents novel Bayesian algorithms for fast inference of neural interactions from spike data, accommodating noise and validated on real and synthetic datasets.

Findings

Algorithms accurately recover known couplings in synthetic data.

Revealed interaction patterns in salamander retinal neurons.

Compared and contrasted with classical cross-correlation analysis.

Abstract

We present two Bayesian procedures to infer the interactions and external currents in an assembly of stochastic integrate-and-fire neurons from the recording of their spiking activity. The first procedure is based on the exact calculation of the most likely time courses of the neuron membrane potentials conditioned by the recorded spikes, and is exact for a vanishing noise variance and for an instantaneous synaptic integration. The second procedure takes into account the presence of fluctuations around the most likely time courses of the potentials, and can deal with moderate noise levels. The running time of both procedures is proportional to the number S of spikes multiplied by the squared number N of neurons. The algorithms are validated on synthetic data generated by networks with known couplings and currents. We also reanalyze previously published recordings of the activity of the salamander retina (including from 32 to 40 neurons, and from 65,000 to 170,000 spikes). We study the dependence of the inferred interactions on the membrane leaking time; the differences and similarities with the classical cross-correlation analysis are discussed.