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 pa