The Agility of a Neuron: Phase Shift Between Sinusoidal Current Input and Firing Rate Curve

Area of Science:

• Computational Neuroscience
• Biophysics

Background:

• Neuron firing rate is a periodic signal in response to periodic input.
• Input signal frequency and background noise influence neuron output.
• Phase shift between input and output signals is a key characteristic.

Purpose of the Study:

• Introduce a new metric, neuron agility, to quantify response speed to periodic input.
• Develop agility score functions for specific neuron models.
• Enable characterization of the neural environment and comparison of neuron models.

Main Methods:

• Derived agility score functions for balanced leaky integrate-and-fire, Hodgkin-Huxley, and Connor-Stevens neuron models.
• Utilized agility scores to analyze phase shifts in response to periodic input signals.

Main Results:

• Agility score functions were successfully derived for three distinct neuron models.
• The agility score allows for the determination of phase shift based on input frequency.
• This metric facilitates normalization and comparison across different neuron models.

Conclusions:

• Neuron agility provides a quantitative measure of a neuron's dynamic response.
• Agility aids in understanding the impact of the neural environment on signal processing.
• The proposed framework enables standardized comparison of neuron model performance.