Hojjatinia Sahar, Aliyari Shoorehdeli Mahdi, Fatahi Zahra, Hojjatinia Zeinab, Haghparast Abbas
Department of Electrical Engineering and Computer Sciences, The Pennsylvania State University, Pennsylvania, USA.
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Basic Clin Neurosci. 2020 Jan-Feb;11(1):79-90. doi: 10.32598/bcn.9.10.435. Epub 2020 Jan 1.
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. Furthermore, it is important to be capable of modeling the potential neural activities to build precise artificial neural networks. The Izhikevich model is one of the simplest biologically-plausible models, i.e. capable of capturing 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 with the neuronal activity of rat brain with great accuracy would make the model effective for future neural network implementations.
Data sampling from two brain regions, the HIP and BLA, was performed by the extracellular recordings of male Wistar 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. In this algorithm, optimization tools, like crossover and mutation, provide the basis for generating model parameters populations. The process of comparison in each iteration leads to the survival of better populations until achieving the optimum solution.
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.
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 networks 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.
识别正常和异常条件下不同脑区潜在的放电模式,有助于我们深入了解大脑神经相互作用层面的事件。此外,能够对潜在的神经活动进行建模对于构建精确的人工神经网络至关重要。Izhikevich模型是最简单的具有生物学合理性的模型之一,即能够捕捉大多数公认的神经元放电模式。这一特性使该模型在模拟大规模神经元网络时效率很高。精确改进Izhikevich模型以适应大鼠大脑的神经元活动,将使该模型在未来神经网络实现中发挥有效作用。
通过雄性Wistar大鼠的细胞外记录,从海马体(HIP)和杏仁核基底外侧核(BLA)这两个脑区进行数据采样,并使用Plexon离线分选仪进行尖峰分类。通过NeuroExplorer和MATLAB进行进一步分析。为了优化Izhikevich模型参数,使用了遗传算法。在该算法中,交叉和变异等优化工具为生成模型参数群体提供了基础。每次迭代中的比较过程会使更优的群体得以保留,直至找到最优解。
在本研究中,识别出了海马体和杏仁核基底外侧核真实单个神经元可能的放电模式。此外,还得到了一个改进的Izhikevich模型。相应地,这些区域神经元的真实神经元放电模式与Izhikevich神经元放电模式的相应情况得到了精确匹配。
本研究旨在提升我们对大脑不同结构中神经相互作用的认识,加速未来大规模神经网络模拟的质量,并降低建模复杂性。通过实施改进的Izhikevich模型,仅插入合理的放电模式并消除不现实的模式,这一目标得以实现。