Modeling spike frequency adaptation through higher-order fractional leaky integrate and fire model.

Area of Science:

• Computational Neuroscience
• Mathematical Biology

Background:

• Spike frequency adaptation is a fundamental property of neuronal excitability.
• Understanding adaptation mechanisms is crucial for modeling neural networks.

Purpose of the Study:

• To introduce and analyze a higher-order fractional leaky integrate-and-fire model.
• To investigate the role of past membrane potential in spike frequency adaptation.
• To examine the effect of noisy input on neuronal adaptation.

Main Methods:

• Development of a higher-order fractional leaky integrate-and-fire model with a derivative exponent from one to two.
• Analysis of the model's behavior under varying input current intensities, including noisy conditions.

Main Results:

• The fractional model demonstrates that past membrane potential has an inhibitory effect, leading to spike frequency adaptation.
• Increased intensity of noisy input current reinforces spike frequency adaptation.

Conclusions:

• The higher-order fractional leaky integrate-and-fire model provides a new framework for studying neuronal adaptation.
• Noisy input significantly impacts and enhances spike frequency adaptation in this model.