Fitting of TC model according to key parameters affecting Parkinson's state based on improved particle swarm...

Area of Science:

• Computational Neuroscience
• Biophysics
• Systems Neuroscience

Background:

• Biophysical neuron models often involve numerous parameters, making them complex to analyze.
• Specific neuronal characteristics, like spiking patterns, are governed by a few critical parameters.
• Thalamic neuron relay reliability is a key factor influencing the state of Parkinson's disease.

Purpose of the Study:

• To develop a method for fitting essential parameters in biophysical neuron models using their spiking characteristics.
• To enhance the traditional particle swarm optimization (PSO) algorithm for improved parameter fitting.
• To investigate the role of specific thalamic neuron parameters in Parkinson's disease.

Main Methods:

• A novel PSO algorithm was developed by integrating a nonlinear concave function and Logistic chaotic mapping to adjust inertia weight.
• This modification aims to prevent premature convergence and local optima during the optimization process.
• Three critical parameters influencing the Parkinson's state in a thalamic neuron model were identified and fitted using the enhanced PSO.

Main Results:

• The improved PSO algorithm successfully fitted key parameters of the thalamic neuron model.
• Reconstruction of the neuron model with fitted parameters accurately predicted spiking trajectories.
• The enhanced PSO demonstrated superior performance in avoiding local optima and achieving faster convergence compared to traditional PSO algorithms.

Conclusions:

• The proposed method effectively fits key parameters of biophysical neuron models based on spiking characteristics.
• The enhanced PSO algorithm offers improved accuracy and efficiency for parameter optimization in computational neuroscience.
• This approach provides a valuable tool for studying neuronal dynamics and their relation to neurological disorders like Parkinson's disease.