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1- Department of Electrical Engineering & Computer Science, The Pennsylvania State University, Pennsylvania, USA
2- Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
3- Neuroscience Research Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- Department of Electrical Engineering, South Tehran branch, Islamic Azad University, Tehran, Iran
Abstract:  
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 the simplest biologically plausible models that is capable of capturing 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 neuron al 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 by Plexon offline sorter. Further analyses are done through NeuroExplorer and MATLAB software. In order to optimize the Izhikevich model parameters, genetic algorithm is used. In this algorithm, optimization tools such as crossover and mutation provide the basis for generation of model parameters populations. The process of comparison in each iteration leads to 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 are identified. Additionally, improvement of the Izhikevich model is achieved. As the result, the real neuronal spiking pattern of these regions’ neurons, and the corresponding cases of the Izhikevich neuron spiking pattern are adjusted with a 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.
 
Type of Study: Original | Subject: Computational Neuroscience
Received: 2018/12/29 | Accepted: 2019/12/2

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