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Updated: Jul 15, 2026
08:34
Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses
Published on: May 9, 2021
Sensitivity versus resonance in two-dimensional spiking-bursting neuron models.
Borja Ibarz1, Gouhei Tanaka, Miguel A F Sanjuán
1Nonlinear Dynamics and Chaos Group, Departamento de Física, Universidad Rey Juan Carlos, Tulipán s/n, 28933 Móstoles, Madrid, Spain. borja.ibarz@urjc.es
Summary
Researchers found a trade-off between neuron sensitivity to stimulation and resonance. This balance, influenced by the slow variable
Area of Science:
- Computational neuroscience
- Mathematical biology
- Neuronal dynamics
Background:
- Spiking and bursting neuron models are crucial for understanding neural computation.
- Map-based models offer simplified yet powerful tools for analyzing neuronal behavior.
- Understanding the interplay between external stimuli and intrinsic neuronal properties is essential.
Purpose of the Study:
- To investigate the relationship between neuronal sensitivity to steady external stimulation and resonance properties.
- To explore how the neutral or asymptotic character of the slow variable influences this trade-off.
- To elucidate the implications for suprathreshold neuronal behavior in isolation and within networks.
Main Methods:
- Phase plane analysis was employed on a class of two-dimensional spiking and bursting neuron models.
- The study encompassed popular map-based neuron models.
- Analysis focused on different regimes: excitable, regular spiking, and bursting.
Main Results:
- A fundamental trade-off exists between a neuron's sensitivity to steady external stimulation and its resonance properties.
- The neutral or asymptotic character of the slow variable can be tuned to modulate this sensitivity-resonance trade-off.
- Specific implications for suprathreshold dynamics were identified across various neuronal regimes.
Conclusions:
- Established a consistent link between single-neuron parameters and emergent network dynamics.
- The findings provide a valuable guide for developing and refining computational neuron models.
- Understanding this trade-off is key for predicting neuronal and network responses to stimuli.
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