7e16b20a9c5c16095296bcd0454bc3c99cbb2969
Recently, Lynch and Houghton (2015) have 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 computational efficient spikee as the fitness function. This combination of algorithms is first tested on simulated data with known parameters, and then validation on a real data set recorded in vivo from auditory neurons in the zebra finch (Taeniopygia guttata).
Genetic
Algorithm
Mathematical Algorithm
Mathematical models such as the HR-neuron provide researchers a framework to understand and predict qualities of a given system of interest. Researchers have developed many methods for finding an optimal set of parameters for a mathematical model