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Recently, Lynch and Houghton (2015) developed a method to predict in vivo 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).

Lynch and Houghton’s method fills a clear need in the neuroinformatics field and moves forward the possibilities for neuron modeling in in vivo electrophysiology research. There exist many avenues for further improvement, including models with more biologically interpretable parameters and improved optimization algorithms.

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 et al., 2013), a Python implementation of a Markov chain Monte Carlo (MCMC), to find a local optima using the computationally efficient 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 in vivo from auditory neurons in the zebra finch (Taeniopygia guttata).