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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
Spike width and frequency alter stability of phase-locking in electrically coupled neurons
Ramana Dodla1, Charles J Wilson
1Department of Biology, University of Texas at San Antonio, San Antonio, TX 78249, USA. ramana.dodla@utsa.edu
Biological Cybernetics
|April 18, 2013
Summary
Synchrony in coupled neurons is stable at low frequencies but can become unstable at high frequencies. Stability switches based on neuron firing properties like spike width and height.
Area of Science:
- Computational neuroscience
- Theoretical neuroscience
- Neuronal dynamics
Background:
- Understanding neuronal synchrony is crucial for brain function.
- Phase-locked states in electrically coupled neurons are fundamental to network dynamics.
- Previous studies often simplify neuron models, limiting applicability.
Purpose of the Study:
- To investigate the stability of phase-locked states in type-1 neurons.
- To analyze how parameters like frequency, spike width, and spike height influence synchrony and antisynchrony.
- To derive analytical expressions for stability boundaries.
Main Methods:
- Utilizing piecewise linear formulations for voltage profiles and phase response curves.
- Analyzing stability by computing the interaction function.
- Determining the boundaries of stability for synchronous and antisynchronous states.
Main Results:
- Synchrony is stable at low frequencies/small spike widths; antisynchrony is unstable.
- Stability reverses at high frequencies/large spike widths.
- Increasing the spike width-to-height ratio promotes stable synchrony over antisynchrony.
- Phase response curve skewness affects synchrony and antisynchrony boundaries.
Conclusions:
- Neuronal synchrony stability is highly sensitive to intrinsic neuronal properties and network frequency.
- The study provides analytical tools to predict phase-locking behavior in neuronal networks.
- These findings offer insights into the mechanisms underlying neural oscillations and information processing.
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