Computational Neuronal Networks
(joint work with P. Molnar and J.J. Hickman)
Research is focused on building data-true neuronal models that can be utilized to build and predict the behavior of neuronal networks. We are especially interested in in vitro systems (being developed in the labs of Drs. Hickman and Molnar) where the environment and measurements can be made uniform across many experiments. Our goal is to derive low-dimensional parametric and nonparametric models that may be deterministic or stochastic so that many such components can be put together to form larger computational systems (with predictive capabilities) that do not become unwieldy and computationally untenable. This work is supported in part by an NIH (NINDS) grant and a seed grant from Office of Research & Commercialization at UCF.