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Generic two-variable model of excitability
A C Ventura1, G B Mindlin, S Ponce Dawson
1Departamento de Física, FCEN, UBA Ciudad Universitaria, Pabellón I (1428), Buenos Aires, Argentina.
Summary
We developed a versatile model for excitable systems, capable of classifying different regimes. This model algorithmically fits experimental data, accurately determining the excitability class of various systems.
Area of Science:
- Computational Neuroscience
- Dynamical Systems Theory
- Biophysics
Background:
- Excitable systems exhibit threshold dynamics, crucial for phenomena like neuronal firing.
- Characterizing diverse excitable regimes remains a challenge in nonlinear dynamics.
- Existing models may not universally capture all classes of excitable behavior.
Purpose of the Study:
- To introduce a unified, simple model for all classes of two-dimensional excitable regimes.
- To demonstrate the model's capability in classifying system excitability.
- To provide a method for algorithmic fitting to experimental and simulated data.
Main Methods:
- Development of a novel, simple two-dimensional model.
- Utilizing a
- standard
- vector field representation for broad applicability.
- Algorithmic fitting of the model to spike data using membrane potential recordings and established models (FitzHugh-Nagumo, Eguía et al.).
Main Results:
- The model successfully displays all classes of two-dimensional excitable regimes.
- One model variable exhibits characteristic spikes, analogous to real-world excitable systems.
- Successful algorithmic fitting and classification of excitability for leech neuron data, FitzHugh-Nagumo, and Eguía et al. models were achieved.
Conclusions:
- The presented model offers a unified framework for analyzing diverse excitable systems.
- Its algorithmic fitting capability allows for robust classification of excitability from various data sources.
- This work simplifies the quantitative analysis and comparison of different excitable phenomena.