HVC Interneuron Properties from Statistical Data Assimilation

TL;DR

This paper uses data assimilation with Hodgkin-Huxley models and experimental recordings to accurately infer parameters of avian HVCI neurons, exploring model variations and data effects.

Contribution

It introduces a method combining biophysical models, experimental data, and data assimilation to precisely infer neuron parameters and ionic currents.

Findings

Multiple valid parameter sets identified by the DA method.

Model accuracy depends on data quality and ionic current composition.

Manipulating data and model components affects inference results.

Abstract

Data assimilation (DA) solves the inverse problem of inferring initial conditions given data and a model. Here we use biophysically motivated Hodgkin-Huxley (HH) models of avian HVCI neurons, experimentally obtained recordings of these neurons, and our data assimilation algorithm to infer the full set of parameters and a minimal set of ionic currents precisely reproducing the observed waveform information. We find many distinct validated sets of parameters selected by our DA method and choice of model. We conclude exploring variations on the inverse problem applied to neurons producing accurate or inaccurate results; by manipulating data presented to the algorithm, varying sample rate and waveform; and by manipulating the model by adding and subtracting ionic currents.