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<h1 class="ltx_title_section">Introduction<br></h1><div><br></div><div>Recently, Lynch and Houghton (2015) developed a method to predict <i>in vivo</i>
response of auditory neurons using an estimated spectrotemporal
receptive field (STRF) filter combined with an integrate-and-fire neuron
model based on the Izhikevich neuron (Izhikevich, 2003). This model
adds an additional adaptive parameter to the integrate-and-fire neuron
for enhanced biophysical realism. Optimization is performed iteratively
on the parameters of the filter and the parameters of the neuron model
using a genetic algorithm in which fitness is evaluated by the van
Rossum distance metric (van Rossum, 2001).<br></div><p>Lynch and
Houghton’s method fills a clear need in the neuroinformatics field and
moves forward the possibilities for neuron modeling in <i>in vivo</i>
electrophysiology research. There exist many avenues for further
improvement, including models with more biologically interpretable
parameters and improved optimization algorithms.<br></p><p>In this
paper, we propose a dynamical systems-based neuron model combined with
an STRF filter that provides superior prediction accuracy with a
computationally efficient optimization algorithm. This method is based
on a Hindmarsh-Rose (HR) neuron model (Hindmarsh & Rose, 1984),
which strikes a balance between the limited parameter sets of the
integrate-and-fire models and the biological realism of the
Hodgkin-Huxley ion current models (Hodgkin & Huxley, 1952). The
feature space is explored by emcee (Foreman-Mackey <i>et al</i>., 2013), a Python implementation of a Markov chain Monte Carlo (MCMC), to find a local optima using the computationally efficient<i> </i>SPIKY synchronization metric (Kreuz et al., 2015) as the fitness function.
This combination of algorithms is first tested on simulated data with
known parameters, and then validated on a real data set recorded <i>in vivo</i> from auditory neurons in the zebra finch (<i>Taeniopygia guttata</i>).<br></p><div><br></div>