Saçu İbrahim Ethem
Clinical Engineering Research and Implementation Center (ERKAM), Erciyes University, 38030 Kayseri, Turkey.
Cogn Neurodyn. 2024 Aug;18(4):1877-1893. doi: 10.1007/s11571-023-10055-z. Epub 2024 Jan 8.
In this study, effects of high-order interactions on synchronization of the fractional-order Hindmarsh-Rose neuron models have been examined deeply. Three different network situations in which first-order coupling, high-order couplings and first-plus second-order couplings included in the neuron models, have been considered, respectively. In order to find the optimal values of the first- and high-order coupling parameters by minimizing the cost function resulted from pairwise and triple interactions, the particle swarm optimization algorithm is employed. It has been deduced from the numerical simulation results that the first-plus second-order couplings induce the synchronization with both reduced first-order coupling strength and total cost compared to the first-order coupled case solely. When the only first-order coupled case is compared with the only second-order coupled case, it is determined that the neural network with only second-order couplings involved could achieve synchronization with lower coupling strength and, as a natural result, lower cost. On the other hand, solely second- and first-plus second-order coupled networks give very similar results each other. Therefore, high-order interactions have a positive effect on the synchronization. Additionally, increasing the network size decreases the values of the both first- and high-order coupling strengths to reach synchronization. However, in this case, total cost should be kept in the mind. Decreasing the fractional order parameter causes slower synchronization due to the decreased frequency of the neural response. On the other hand, more synchronous network is possible with increasing the fractional order parameter. Thus, the neural network with higher fractional order as well as high-order coupled is a good candidate in terms of the neural synchronization.
The online version contains supplementary material available at 10.1007/s11571-023-10055-z.
在本研究中,深入研究了高阶相互作用对分数阶 Hindmarsh-Rose 神经元模型同步的影响。分别考虑了神经元模型中包含一阶耦合、高阶耦合和一阶加二阶耦合的三种不同网络情况。为了通过最小化由成对和三重相互作用产生的成本函数来找到一阶和高阶耦合参数的最优值,采用了粒子群优化算法。从数值模拟结果推断,与仅一阶耦合的情况相比,一阶加二阶耦合在降低一阶耦合强度和总成本的同时诱导同步。当仅一阶耦合的情况与仅二阶耦合的情况进行比较时,确定仅涉及二阶耦合的神经网络可以以较低的耦合强度实现同步,结果自然是成本较低。另一方面,仅二阶耦合网络和一阶加二阶耦合网络彼此给出非常相似的结果。因此,高阶相互作用对同步有积极影响。此外,增加网络规模会降低一阶和高阶耦合强度的值以达到同步。然而,在这种情况下,应牢记总成本。分数阶参数的减小会由于神经响应频率的降低而导致同步变慢。另一方面,随着分数阶参数的增加,更同步的网络是可能的。因此,就神经同步而言,具有较高分数阶以及高阶耦合的神经网络是一个很好的选择。
在线版本包含可在 10.1007/s11571-023-10055-z 获得的补充材料。