Statistical data assimilation for estimating electrophysiology simultaneously with connectivity within a biological neuronal network

TL;DR

This paper presents a data assimilation method to estimate neuronal electrophysiological parameters and connectivity in small biological networks, effectively identifying functional modes and reducing model complexity.

Contribution

It introduces a systematic DA approach that simultaneously estimates neuron parameters and synaptic connectivity, handling non-convex optimization and model pruning.

Findings

Successfully recovers network activity modes with chaotic stimuli and voltage measurements

Prunes high-dimensional models to essential parameters

Demonstrates potential for guiding laboratory experiments

Abstract

A method of data assimilation (DA) is employed to estimate electrophysiological parameters of neurons simultaneously with their synaptic connectivity in a small model biological network. The DA procedure is cast as an optimization, with a cost function consisting of both a measurement error and a model error term. An iterative reweighting of these terms permits a systematic method to identify the lowest minimum, within a local region of state space, on the surface of a non-convex cost function. In the model, two sets of parameter values are associated with two particular functional modes of network activity: simultaneous firing of all neurons, and a pattern-generating mode wherein the neurons burst in sequence. The DA procedure is able to recover these modes if: i) the stimulating electrical currents have chaotic waveforms, and ii) the measurements consist of the membrane voltages of all neurons in the circuit. Further, this method is able to prune a model of unnecessarily high dimensionality to a representation that contains the maximum dimensionality required to reproduce the provided measurements. This paper offers a proof-of-concept that DA has the potential to inform laboratory designs for estimating properties in small and isolatable functional circuits.