Improving the Izhikevich Model Based on Rat Basolateral Amygdala and Hippocampus Neurons, and Recognizing Their Possible Firing Patterns

TL;DR

This paper enhances the Izhikevich neuron model to better replicate firing patterns of rat hippocampus and amygdala neurons, improving neural network simulations and understanding of brain region interactions.

Contribution

The study introduces an optimized Izhikevich model tailored to rat brain neurons using genetic algorithms, capturing realistic firing patterns with high accuracy.

Findings

Identified firing patterns of HIP and BLA neurons.

Developed an improved Izhikevich model matching real neuron activity.

Enhanced model reduces complexity and increases simulation accuracy.

Abstract

Introduction- Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of neural interactions in the brain. The Izhikevich model is one of the simplest biologically plausible models, i.e. capable of capturing the most recognized firing patterns of neurons. This property makes the model efficient in simulating the large-scale networks of neurons. Improving the Izhikevich model for adapting to the neuronal activity of the rat brain with great accuracy would make the model effective for future neural network implementations. Methods- Data sampling from two brain regions, the HIP and BLA, was performed by the extracellular recordings of male rats, and spike sorting was conducted by Plexon offline sorter. Further analyses were performed through NeuroExplorer and MATLAB. To optimize the Izhikevich model parameters, a genetic algorithm was used. The process of comparison in each iteration leads to the survival of better populations until achieving the optimum solution. Results- In the present study, the possible firing patterns of the real single neurons of the HIP and BLA were identified. Additionally, an improved Izhikevich model was achieved. Accordingly, the real neuronal spiking pattern of these regions neurons and the corresponding cases of the Izhikevich neuron spiking pattern were adjusted with great accuracy. Conclusion- This study was conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large-scale neural network simulations, as well as reducing the modeling complexity. This aim was achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns, and eliminating unrealistic ones.