Nonlinear Statistical Data Assimilation for HVC$_{\text{RA}}$ Neurons in the Avian Song System

TL;DR

This paper develops a nonlinear statistical data assimilation method to estimate parameters and unmeasured variables in a biophysical model of HVC$_{ ext{RA}}$ neurons, facilitating experimental design and model validation in the avian song system.

Contribution

It introduces a robust, time-scale separation approach for parameter estimation in a detailed neuron model using only somatic voltage data.

Findings

The method accurately predicts neuron behavior with complete observations.

Time-scale separation improves numerical stability in parameter estimation.

Framework supports experimental design for slice preparations.

Abstract

With the goal of building a model of the HVC nucleus in the avian song system, we discuss in detail a model of HVC$_{\text{RA}}$ projection neurons comprised of a somatic compartment with fast Na$^+$ and K$^+$ currents and a dendritic compartment with slower Ca$^{2+}$ dynamics. We show this model qualitatively exhibits many observed electrophysiological behaviors. We then show in numerical procedures how one can design and analyze feasible laboratory experiments that allow the estimation of all of the many parameters and unmeasured dynamical variables, given observations of the somatic voltage $V_s(t)$ alone. A key to this procedure is to initially estimate the slow dynamics associated with Ca, blocking the fast Na and K variations, and then with the Ca parameters fixed, estimate the fast Na and K dynamics. This separation of time scales provides a numerically robust method for completing the full neuron model, and the efficacy of the method is tested by prediction when observations are complete. The simulation provides a framework for the slice preparation experiments and illustrates the use of data assimilation methods for the design of those experiments.