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.
翻译:在正常和异常条件下,根据不同的大脑区域确定潜在发烧模式,这提高了我们对大脑神经互动水平上的事件的理解。Izhikevich模型是生物学上最简单、最可信的模型之一,即能够捕捉最公认的神经元的发射模式。这种属性使得模型在模拟大规模神经网络时具有效率。用非常精确的方式改进Izhikevich模型,以适应大鼠大脑神经活动,将使该模型对未来神经网络的运行产生实效。两个脑区域,即HIP和BLA的数据取样,由雄性大鼠的超细胞记录进行,由Plexalon离线排序机组进行加压排序。通过NeuroExplor和MATLAB进行了进一步分析。为优化Izikevich模型参数,使用了基因算法。对每升温模型的比较过程导致更好的人群生存,直到实现最佳的解决方案。结果-研究中,通过雄性智能智能智能智能网络的超模级神经元和BLA的精度记录进行模拟测。随后,对Izhenical Stal Stal的系统结构结构进行了改进了Slal-stal deal Stal Stal Stal Stal Stal Stal Stal Stal Stal 和直观结构进行了分析。