About my Research

My current research projects are focused on the analysis of biological rhythms, model estimation algorithms, qualitative reasoning for continuous-time dynamical systems, the utilization of dynamical systems theory to analyze and reduce complex neuroscience models, and software for neuroinformatics. This work is supported by an NSF grant from the Emerging Models and Technologies program of the Division of Computer and Communication Foundations. The title is "A Computational Framework for Inferring Self-Regulatory Properties from High-Dimensional Dynamic Models of Biological Systems". See the introductions to some of these topic areas at the following links to Wikipedia and Scholarpedia: computational neuroscience, neuroinformatics, phase response curves (general oscillators), phase response curves (neural dynamics) and neuronal parameter optimization.

I received two internal grants (URSA Research Initiation Grant and Brains & Behavior Seed Grant) to support research initiation in my first year at Georgia State.

I am the primary developer for the PyDSTool dynamical systems software package, which implements various ideas being developed in my research through the Python language. This software is open-source freeware, is actively maintained, and is available at Sourceforge. I also belong to the Neural Ensemble online community, which focuses on python software tools for neuroscience.

I co-run the Spineless Neural Systems group with Paul Katz. Here is a useful collaborative tool for invertebrate neuroscience.

I belong to the INCF Task Force for a Standard Language in Neural Network Modeling, working on future markup language constructs based on XML. (INCF stands for International Neuroinformatics Coordinating Facility)

I compiled and edit the Guide to good practice for research in experimentally data-driven computational modeling and simulation.

I wrote and maintain a handy little XPP-Matlab interface between Bard Ermentrout's XPP-AUT software and Matlab. This simple code makes batch-running from external scripts very easy!

I contribute to the SoftWare Interest Group at Georgia State.

Open Positions

I have an NSF-funded PhD position available in computational neuroscience.

I also have a PhD position available in integrative biomechanics, computational modeling, and nonlinear dynamics, to be supervised jointly with Don Edwards.

Recent Publications

• R. Clewley, Encoding the Fine-Structured Mechanism of Action Potential Dynamics with Qualitative Motifs. Under review at the Journal of Computational Neuroscience.
• R. Clewley, C. Soto-Trevino, F. Nadim, Dominant ionic mechanisms explored in the transition between spiking and bursting using local low-dimensional reductions of a biophysically realistic model neuron, Journal of Computational Neuroscience, 26(1), 75-90, 2009.
• R. H. Clewley, J. M. Guckenheimer, F. J. Valero-Cuevas, Estimating effective degrees of freedom in motor systems, IEEE Transactions on Biomedical Engineering, 55(2), 430-442, 2008. [Preprint]
• R. H. Clewley, J. M. Guckenheimer, F. J. Valero-Cuevas, Estimating degrees of freedom in motor systems, arxiv:q-bio.QM/0610058, October 2006. (Extended version of the IEEE TBME paper.)
• R. Clewley, H. G. Rotstein, N. Kopell, A Computational Tool for the Reduction of Nonlinear ODE Systems possessing Multiple Scales, Multiscale Modeling and Simulation, 4(3), 732-759, 2005. [Preprint]
• T. Netoff, R. Clewley, S. Arno, T. Keck, J. White, Epilepsy in Small World Networks, The Journal of Neuroscience, 24(37):8075 - 8083, 2004. [Preprint, and extended derivations of some of our terms are in this presentation.]
• R. Clewley, Dominant Scale Analysis for Automatic Reduction of High Dimensional ODE Systems, ICCS Proceedings 2004 (to appear in print in 2010).

Other Research Links

Ron Calabrese and Rob Butera gave interesting Long View seminars at GSU in 2008.

Teaching

Calculus I - MATH 2211

I received a GSU STEM Faculty Fellowship award in the Summer of 2009 to develop materials for a more science-focused version of the standard Calculus I class that we offer. This class is now offered specially for science majors in Spring 2010.

Numerical analysis I - MATH 4610/6610

Fall 2009. Details on this course can be found here.

A useful tool in learning the basics of computer arithmetic is this python module, which simulates binary floating point representation to IEEE 754 standards of arbitrary fixed precision, or to infinite precision.

This page is still in development. For other information, please see my old page at Cornell.