Mesoscopic modeling of hidden spiking neurons

TL;DR

This paper introduces neuLVM, a mesoscopic latent variable model for spiking neural networks that effectively infers unobserved neuron activity and connectivity from limited observed data, with strong biological interpretability.

Contribution

The work develops neuLVM, a novel biologically grounded mesoscopic model that enables efficient inference of hidden neuron activity and network parameters from sparse observations.

Findings

neuLVM accurately recovers connectivity parameters

It infers single-trial latent population activity

It reproduces metastable dynamics and generalizes to perturbations

Abstract

Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstrained problems that are hard to tackle with maximum likelihood estimation. In this work, we use coarse-graining and mean-field approximations to derive a bottom-up, neuronally-grounded latent variable model (neuLVM), where the activity of the unobserved neurons is reduced to a low-dimensional mesoscopic description. In contrast to previous latent variable models, neuLVM can be explicitly mapped to a recurrent, multi-population SNN, giving it a transparent biological interpretation. We show, on synthetic spike trains, that a few observed neurons are sufficient for neuLVM to perform efficient model inversion of large SNNs, in the sense that it can recover connectivity parameters, infer single-trial latent population activity, reproduce ongoing metastable dynamics, and generalize when subjected to perturbations mimicking photo-stimulation.