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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
Parameter-sweeping techniques for temporal dynamics of neuronal systems: case study of Hindmarsh-Rose model
Roberto Barrio1, Andrey Shilnikov
1Neuroscience Institute and Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, 30303, USA. ashilnikov@gsu.edu.
Journal of Mathematical Neuroscience
|June 5, 2012
Summary
We developed computational tools for analyzing neuronal dynamics, finding they accurately identify complex behaviors and bifurcations in models. These tools aid in understanding neural function and disease.
Area of Science:
- Computational Neuroscience
- Nonlinear Dynamics
Background:
- Effective numerical tools are crucial for studying nonlinear dynamics in life sciences.
- Neuronal models exhibit complex dynamics requiring advanced analytical methods.
Purpose of the Study:
- To develop and validate a suite of computational tools for analyzing neuronal dynamics.
- To compare the effectiveness of novel techniques with established calculus-based methods.
Main Methods:
- Developed a computational tool suite for two-parameter screening of neuronal dynamics.
- Applied techniques to analyze temporal characteristics (duty cycle, interspike interval, spike number) in the Hindmarsh-Rose model.
- Compared results with Lyapunov exponent calculations for nonlinear systems analysis.
Main Results:
- The developed tools accurately screen dynamics in neuronal models.
- Novel techniques demonstrated effectiveness comparable to Lyapunov exponent analysis.
- Both methods successfully identified complex dynamics and bifurcations in the Hindmarsh-Rose model.
Conclusions:
- The computational suite effectively analyzes complex neuronal dynamics and bifurcations.
- Future work will extend the tools to analyze polyrhythmic bursting patterns in neural networks.
