Borges R R, Borges F S, Lameu E L, Batista A M, Iarosz K C, Caldas I L, Antonopoulos C G, Baptista M S
Pós-Graduação em Ciências, Universidade Estadual de Ponta Grossa, Ponta Grossa, PR, Brazil; Departamento de Matemática, Universidade Tecnológica Federal do Paraná, 86812-460, Apucarana, PR, Brazil.
Pós-Graduação em Ciências, Universidade Estadual de Ponta Grossa, Ponta Grossa, PR, Brazil.
Neural Netw. 2017 Apr;88:58-64. doi: 10.1016/j.neunet.2017.01.010. Epub 2017 Jan 31.
We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently their connections and behavior, the so called spike timing-dependent plasticity (STDP), as a function of their synchronous behavior. We consider STDP of excitatory and inhibitory synapses driven by Hebbian rules. We show that the final state of networks evolved by a STDP depend on the initial network configuration. Specifically, an initial all-to-all topology evolves to a complex topology. Moreover, external perturbations can induce co-existence of clusters, those whose neurons are synchronous and those whose neurons are desynchronous. This work reveals that STDP based on Hebbian rules leads to a change in the direction of the synapses between high and low frequency neurons, and therefore, Hebbian learning can be explained in terms of preferential attachment between these two diverse communities of neurons, those with low-frequency spiking neurons, and those with higher-frequency spiking neurons.
我们研究了网络中霍奇金-赫胥黎神经元暂时或永久改变其连接和行为的能力,即所谓的尖峰时间依赖可塑性(STDP),作为其同步行为的函数。我们考虑由赫布规则驱动的兴奋性和抑制性突触的STDP。我们表明,由STDP演化的网络的最终状态取决于初始网络配置。具体而言,初始的全对全拓扑会演化为复杂拓扑。此外,外部扰动可诱导簇的共存,即神经元同步的簇和神经元不同步的簇。这项工作揭示,基于赫布规则的STDP会导致高频和低频神经元之间突触方向的改变,因此,赫布学习可以用这两个不同神经元群体之间的优先连接来解释,即低频发放神经元群体和高频发放神经元群体。