Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
Neural Netw. 2019 Apr;112:15-23. doi: 10.1016/j.neunet.2019.01.002. Epub 2019 Jan 16.
Cortical neural connectivity has been shown to exhibit a small-world (SW) network topology. However, the role of the topology in neural information processing remains unclear. In this study, we investigated the learning performance of an echo state network (ESN) that includes the SW topology as a reservoir. To elucidate the potential of the SW topology, we limited the numbers of the input and output nodes in the ESN and spatially segregated the output nodes from the input nodes. We tested the ESNs in two benchmark tasks: memory capacity and nonlinear time-series prediction. The SW-ESN exhibited the best learning performance when the spectral radius of the weight matrix was large and when the input and output nodes were segregated. That is, the SW topology provided the ESN with a stable echo state property over a broad range of the weight matrix and efficiently propagated input signals to the output nodes. This result is the same as that of the ESN using a real human cortical connectivity. Thus, the results suggest that the SW topology is essential for maintaining the echo state property, which is the appropriate neural dynamics between input and output brain regions.
皮层神经连接被证明具有小世界（SW）网络拓扑结构。然而，拓扑结构在神经信息处理中的作用尚不清楚。在这项研究中，我们研究了包含 SW 拓扑结构作为储层的回声状态网络 (ESN) 的学习性能。为了阐明 SW 拓扑结构的潜力，我们限制了 ESN 中的输入和输出节点的数量，并将输出节点与输入节点空间分离。我们在两个基准任务中测试了 ESN：存储容量和非线性时间序列预测。当权重矩阵的谱半径较大且输入和输出节点分离时，SW-ESN 表现出最佳的学习性能。也就是说，SW 拓扑结构在权重矩阵的广泛范围内为 ESN 提供了稳定的回声状态特性，并有效地将输入信号传播到输出节点。该结果与使用真实人类皮质连通性的 ESN 相同。因此，研究结果表明，SW 拓扑结构对于维持回声状态特性至关重要，而回声状态特性是输入和输出脑区之间适当的神经动力学。
