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*).

Margot10 months ago · PublicMaybe the best thing to do for the final is to just do methods and results for the 1D stuff. Leave the rest until we have actual results.