Kanagaraj Sathiyadevi, Moroz Irene, Durairaj Premraj, Karthikeyan Anitha, Rajagopal Karthikeyan
Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, Tamilnadu 600 069 India.
Mathematical Institute, Oxford University, Oxford, OX2 6GG UK.
Cogn Neurodyn. 2024 Apr;18(2):473-484. doi: 10.1007/s11571-023-10000-0. Epub 2023 Aug 24.
In this study, the hybrid conductance-based adaptive exponential integrate and fire (CadEx) neuron model is proposed to determine the effect of magnetic flux on conductance-based neurons. To begin with, bifurcation analysis is carried out in relation to the input current, resetting parameter, and adaptation time constant in order to comprehend dynamical transitions. We exemplify that the existence of period-1, period-2, and period-4 cycles depends on the magnitude of input current via period doubling and period halving bifurcations. Furthermore, the presence of chaotic behavior is discovered by varying the adaptation time constant via the period doubling route. Following that, we examine the network behavior of CadEx neurons and discover the presence of a variety of dynamical behaviors such as desynchronization, traveling chimera, traveling wave, imperfect chimera, and synchronization. The appearance of synchronization is especially noticeable when the magnitude of the magnetic flux coefficient or the strength of coupling strength is increased. As a result, achieving synchronization in CadEx is essential for neuron activity, which can aid in the realization of such behavior during many cognitive processes.
在本研究中，提出了基于混合电导的自适应指数积分发放（CadEx）神经元模型，以确定磁通量对基于电导的神经元的影响。首先，针对输入电流、重置参数和适应时间常数进行分岔分析，以理解动态转变。我们通过倍周期分岔和周期减半分岔举例说明，1周期、2周期和4周期循环的存在取决于输入电流的大小。此外，通过倍周期路径改变适应时间常数，发现了混沌行为的存在。随后，我们研究了CadEx神经元的网络行为，发现了诸如去同步、行波奇美拉、行波、不完全奇美拉和同步等多种动态行为。当磁通量系数的大小或耦合强度增加时，同步的出现尤为明显。因此，在CadEx中实现同步对于神经元活动至关重要，这有助于在许多认知过程中实现此类行为。
