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Characterizing mixed mode oscillations shaped by noise and bifurcation structure
Peter Borowski1, Rachel Kuske, Yue-Xian Li
1Department of Mathematics, University of British Columbia, Vancouver V6T 1Z2, Canada. peterphysik@gmail.com
Chaos (Woodbury, N.Y.)
|January 5, 2011
Summary
This study introduces new measures to differentiate between neuronal models generating mixed mode oscillations (MMOs). These methods analyze subthreshold dynamics to classify MMO mechanisms, aiding in understanding complex neuronal behavior.
Area of Science:
- Neuroscience
- Computational Biology
- Dynamical Systems
Background:
- Neuronal systems often exhibit mixed mode oscillations (MMOs), characterized by small amplitude oscillations interspersed with spikes.
- Both deterministic and stochastic models exist for MMO generation, but distinguishing their underlying mechanisms is challenging.
- Stochastic models can produce MMOs via noise-driven mechanisms distinct from deterministic routes.
Purpose of the Study:
- To develop and present a suite of quantitative measures for distinguishing between different models and classifying routes to MMO generation.
- To analyze the influence of model parameters, resets, and return mechanisms on MMO dynamics.
- To provide a novel approach using noise levels to differentiate model types and MMO mechanisms.
Main Methods:
- Focusing on subthreshold oscillations, analysis includes interspike interval density, amplitude trends, and a coherence measure.
- Measures were developed and tested on a biophysical model for stellate cells and a FitzHugh-Nagumo-type model.
- Application to related models and exploration of noise level as a distinguishing factor.
Main Results:
- The developed measures effectively distinguish between different MMO-generating models and classify underlying mechanisms.
- Analysis revealed the significant influence of model parameters and reset/return mechanisms on MMO characteristics.
- Noise level was identified as a key factor in differentiating model types and MMO generation routes.
Conclusions:
- The suite of measures provides a robust framework for analyzing and classifying MMOs in computational models.
- These methods can be applied to experimental time series to elucidate underlying dynamical structures.
- The approach allows for the exploitation of intrinsic or extrinsic noise to reveal system dynamics.
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