Mesoscopic modeling of hidden spiking neurons

• URL: http://arxiv.org/abs/2205.13493v1
• Date: Thu, 26 May 2022 17:04:39 GMT
• Title: Mesoscopic modeling of hidden spiking neurons
• Authors: Shuqi Wang, Valentin Schmutz, Guillaume Bellec, Wulfram Gerstner
• Abstract summary: We use coarse-graining and mean-field approximations to derive a bottom-up, neuronally-grounded latent variable model (neuLVM) neuLVM can be explicitly mapped to a recurrent, multi-population spiking neural network (SNN) We show, on synthetic spike trains, that a few observed neurons are sufficient for neuLVM to perform efficient model inversion of large SNNs.
• Score: 3.6868085124383616
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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