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Type I membranes, phase resetting curves, and synchrony
1Department of Mathematics, University of Pittsburgh, PA 15260, USA.
Neural Computation
|July 1, 1996
Summary
Type I membrane models exhibit difficult synchrony and strictly positive phase resetting curves, a general property confirmed by singular perturbation methods. Type II resetting occurs in models with Hopf bifurcations, differing from Type I dynamics.
Area of Science:
- Computational Neuroscience
- Mathematical Biology
- Systems Neuroscience
Background:
- Type I membrane oscillator models, like the Connor and Morris-Lecar models, exhibit very low-frequency oscillations near critical applied currents.
- Previous numerical studies indicated difficulties in achieving synchrony and strictly positive phase resetting curves for these models.
Purpose of the Study:
- To analytically demonstrate that difficult synchrony and strictly positive phase resetting are general properties of Type I membrane models.
- To investigate the occurrence of Type II resetting in models utilizing Hopf bifurcations.
- To differentiate the effects of fast and slow synapses and derive a canonical form for phase interactions.
Main Methods:
- Application of singular perturbation methods.
- Utilizing averaging techniques.
- Analysis of models exhibiting rhythmicity via Hopf bifurcations.
Main Results:
- Singular perturbation and averaging confirm that difficult synchrony and strictly positive phase resetting are general properties of Type I membrane models.
- Type II resetting is shown to occur in models that achieve rhythmicity through a Hopf bifurcation.
- Differences between rapid and slow synapses are elucidated, and a canonical form for phase interactions is derived.
Conclusions:
- The study provides analytical evidence for the inherent properties of Type I membrane models regarding synchrony and phase resetting.
- A distinction is made between Type I and Type II resetting behaviors based on the underlying bifurcation mechanisms.
- The findings offer insights into synaptic dynamics and provide a generalized framework for phase interactions in neural models.