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Introduction

Recently, class="ltx_title_section">


Introduction

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  

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 SPIKE  synchronization 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).