Depto. de Electrónica, CUCEI, Universidad de Guadalajara, Av. Revolución No. 1500, Guadalajara, Jal. C.P. 44430, Mexico.
Comput Math Methods Med. 2021 Jun 16;2021:9956319. doi: 10.1155/2021/9956319. eCollection 2021.
A model of time-dependent structural plasticity for the synchronization of neuron networks is presented. It is known that synchronized oscillations reproduce structured communities, and this synchronization is transient since it can be enhanced or suppressed, and the proposed model reproduces this characteristic. The evolutionary behavior of the couplings is comparable to those of a network of biological neurons. In the structural network, the physical connections of axons and dendrites between neurons are modeled, and the evolution in the connections depends on the neurons' potential. Moreover, it is shown that the coupling force's function behaves as an adaptive controller that leads the neurons in the network to synchronization. The change in the node's degree shows that the network exhibits time-dependent structural plasticity, achieved through the evolutionary or adaptive change of the coupling force between the nodes. The coupling force function is based on the computed magnitude of the membrane potential deviations with its neighbors and a threshold that determines the neuron's connections. These rule the functional network structure along the time.
提出了一种用于神经元网络同步的时变结构可塑性模型。众所周知，同步振荡再现了结构化的群落，并且这种同步是瞬时的，因为它可以被增强或抑制，而所提出的模型再现了这种特性。耦合的进化行为类似于生物神经元网络的行为。在结构网络中，模拟了神经元之间轴突和树突的物理连接，并且连接的演化取决于神经元的势。此外，还表明，耦合力的函数表现为自适应控制器，使网络中的神经元同步。节点度的变化表明，通过节点之间的耦合力的进化或自适应变化，网络表现出时变结构可塑性。耦合力函数基于与邻居的膜电位偏差的计算幅度以及确定神经元连接的阈值。这些沿着时间控制功能网络结构。
