Chen Chun-Chung, Jasnow David
Physics Division, National Center for Theoretical Sciences, Hsinchu, Taiwan 300, Republic of China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Sep;84(3 Pt 1):031908. doi: 10.1103/PhysRevE.84.031908. Epub 2011 Sep 7.
We consider a fully connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low- and high-activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.
我们考虑一个具有脉冲时间依赖可塑性的全连接泄漏积分发放神经元网络。可塑性由一个参数控制，该参数表示以相同平均频率随机发放脉冲的神经元之间突触的预期权重。对于可塑性参数的低值，系统活动由噪声主导，而可塑性参数的高值会导致网络中的自持活动。我们在具有多达128个神经元的有限大小网络上进行事件驱动模拟，以找到不同可塑性参数值下的稳态突触权重构型。在低活动和高活动状态下，突触权重都围绕可塑性参数值呈窄分布，这与平均场理论的预测一致。然而，在两种状态之间的过渡区域，分布会变宽，代表出现的网络结构。使用一种伪物理方法进行可视化，我们表明出现的结构是“路径”或“枢纽”类型，在过渡区域的不同可塑性参数值处观察到。
