Abstract:Introduction: Identifying the potential firing patterns following by different brain regions under normal and abnormal conditions increases our understanding of what is happening in the level of neural interactions in the brain. On the other hand, it is important to be capable of modeling the potential neural activities, in order to build precise artificial neural networks. The Izhikevich model is one of the simple biologically plausible models that is capable of capturing the most known firing patterns of neurons. This property makes the model efficient in simulating large-scale networks of neurons. Improving the Izhikevich model for adapting with the neuronal activity of 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, is performed by extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter. Further data analyses are done through NeuroExplorer and MATLAB software. In order to optimize the Izhikevich model parameters, the genetic algorithm is used. Results: In the present study, the possible firing patterns of the real single neurons of the HIP and BLA are identified. Additionally, improvement of the Izhikevich model is achieved. As a result, the real neuronal spiking pattern of these regions neurons, and the corresponding cases of the Izhikevich neuron spiking pattern are adjusted with great accuracy. Conclusion: This study is conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large scale neural networks simulations, as well as reducing the modeling complexity. This aim is achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns and eliminating unrealistic ones, as the results of this study.