Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density

TL;DR

This paper introduces a particle filtering approach to infer neural signals and parameters from noisy voltage recordings, enabling detailed reconstruction of neuron activity and synaptic inputs with high accuracy.

Contribution

It develops a novel particle filtering algorithm with optimal importance density for estimating neural activity, parameters, and synaptic conductances from intracellular recordings.

Findings

Accurately reconstructs voltage traces and auxiliary variables.

Estimates physiological parameters like conductances and reversal potentials.

Achieves bound-optimal performance even during spiking activity.

Abstract

This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain's connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by stochastic filtering, that allow a probabilistic interpretation and treatment of the problem. Using particle filtering we are able to reconstruct traces of voltages and estimate the time course of auxiliary variables. By extending the algorithm, through PMCMC methodology, we are able to estimate hidden physiological parameters as well, like intrinsic conductances or reversal potentials. Last, but not least, the method is applied to estimate synaptic conductances arriving at a target cell, thus reconstructing the synaptic excitatory/inhibitory input traces. Notably, these estimations have a bound-achieving performance even in spiking regimes.