Convolutional method for data assimilation An improved method on neuronal electrophysiological data

TL;DR

This paper introduces a convolutional data assimilation technique for neuronal electrophysiology that improves synchronization and parameter estimation by considering spike timing and amplitude, outperforming traditional methods.

Contribution

It presents a novel convolution-based approach that enhances data assimilation in neural signals by incorporating spike timing and amplitude alignment, compatible with gradient optimization.

Findings

Improved parameter estimation accuracy in neural data.

Enhanced prediction of sharp, time-sensitive neural dynamics.

Validated on hippocampal neuron recordings.

Abstract

We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. While conventional methods rely on nudging terms and pointwise deviation metrics, they often fail to account for spike timing precision, a key feature in neural signals. Our approach applies a Gaussian convolution to both measured data and model estimates, enabling a cost function that evaluates both amplitude and timing alignment via spike overlap. This formulation remains compatible with gradient-based optimization. Through twin experiments and real hippocampal neuron recordings, we demonstrate improved parameter estimation and prediction quality, particularly in capturing sharp, time-sensitive dynamics.