<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 &amp; 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 &amp; 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 &nbsp;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>