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Dr. Epaminondas Rosa Jr.

Moulton Hall - MLT 302
Office Hours
By appointment
  • About
  • Education
  • Research

Current Courses

108.001College Physics I

299.010Independent Honor Study

290.010Research In Physics

108.001College Physics I

108.002College Physics I

108.003College Physics I

108.004College Physics I

390.010Computational Research In Physics

286.009Honors Undergraduate Research II

299.010Independent Honor Study

284.001Quantum Mechanics I

290.010Research In Physics

Teaching Interests & Areas

Dr. Rosa's teaching experience includes Physics for Engineers, Atoms to Galaxies, Physics I, Thermal Physics, Statistical Mechanics, Quantum Mechanics, Nonlinear Dynamics and Chaos Theory, and Computational Neuroscience.

Research Interests & Areas

Dr. Rosa's research work is in the field of computational neuroscience and nonlinear dynamics, with special emphasis on synchronization of complex systems. A particular complex system of interest is networks of neuron. Synchronous neurons are critical in mechanisms associated with rhythmic motions such as mastication, breathing, walking, swimming and flying. Abnormal synchrony has been associated with neurological disorders such as epilepsy, Parkinson's disease, and depression, and in many processes associated with circadian rhythms. Synchronization is also directly related to memory and information processing.

Ph D Chemical-Physics

University of Minnesota

MS Physics

Federal University of Parana

BE Civil Engineering

Federal University of Parana

Journal Article

Rutherford, G., Mobille, Z., Brandt-Trainer, J., Follmann, R., & Rosa, E. Analog implementation of a Hodgkin-Huxley model neuron. AMERICAN JOURNAL OF PHYSICS 88.11 (2020): 918-923.
Burek, M., Follmann , R., & Rosa, E. Temperature effects on neuronal firing rates and tonic-to-bursting transitions. BIOSYSTEMS 180 (2019): 1-6.
Follmann , R., & Rosa, E. Predicting slow and fast neuronal dynamics with machine learning. CHAOS 29.11 (2019)
Follmann , R., & Rosa, Epaminondas, Jr.. Predicting slow and fast neuronal dynamics with machine learning. CHAOS 29.11 (2019)
Follmann , R., Shaffer, A., Mobille, Z., Rutherford, G., & Rosa, E. Synchronous tonic-to-bursting transitions in a neuronal hub motif. CHAOS 28.10 (2018)


Reservoir Computing: Structure analysis and dynamics predictability, APS March Meeting 2022, Volume 67, Number 3, March 2022, Chicago, IL.
G. Katuri, E. Rosa, and R. Follmann. 2022. Detecting synchronization in brain activity. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '22). Association for Computing Machinery, New York, NY, USA, Article 66, 1.
Using Reservoir Computing for Predicting Slow and Fast Neuronal Dynamics. SIAM Conference on Applications of Dynamical Systems. Society for Industrial and Applied Mathematics. May 2021
A Characteristic Firing Rate in the Huber-Braun Model Neurons. Workshop on Theories, Models, and Experiments for Life Science Research and Education. Phillips University. (2017)
Dynamics and Synchronization of Model Neurons I. Argonne Undergraduate Research Symposium. (2012)
Dynamics and Synchronization of Model Neurons II. Argonne Undergraduate Research Symposium. (2012)
E=mc2, computer simulations of particles creation by strong laser fields. Illinois State University, Department of Physics. (2012)
Impulse Dynamics of Coupled Synchronous Neurons. Computational Neuroscience Meeting. (2012)
Mathematical Modeling of Neurons. Federal University of Rio Grande do Sul Colloquium. Federal University of Rio Grande do Sul. (2012)
Mathematical Modeling of Neurons. Federal University of Rio Grande do Sul – Porto Alegre. (2012)

Grants & Contracts

Mathematical Modeling and Experimental Work on the Stomatogastric Nervous System of the Crab Cancer Borealis to study sensory-motor interactions.. Cross-Disciplinary Grant Development Program. Illinois State University. (2013)