Tian Gengshuo, Li Shangyang, Huang Tiejun, Wu Si
School of Electronics Engineering and Computer Science, Peking University, Beijing, China.
IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Front Comput Neurosci. 2020 Sep 3;14:79. doi: 10.3389/fncom.2020.00079. eCollection 2020.
Excitation-inhibition (E-I) balanced neural networks are a classic model for modeling neural activities and functions in the cortex. The present study investigates the potential application of E-I balanced neural networks for fast signal detection in brain-inspired computation. We first theoretically analyze the response property of an E-I balanced network, and find that the asynchronous firing state of the network generates an optimal noise structure enabling the network to track input changes rapidly. We then extend the homogeneous connectivity of an E-I balanced neural network to include local neuronal connections, so that the network can still achieve fast response and meanwhile maintain spatial information in the face of spatially heterogeneous signal. Finally, we carry out simulations to demonstrate that our model works well.
兴奋-抑制（E-I）平衡神经网络是用于模拟皮层神经活动和功能的经典模型。本研究探讨了E-I平衡神经网络在脑启发计算中进行快速信号检测的潜在应用。我们首先从理论上分析了E-I平衡网络的响应特性，发现网络的异步发放状态产生了一种最优噪声结构，使网络能够快速跟踪输入变化。然后，我们将E-I平衡神经网络的均匀连接扩展到包括局部神经元连接，以便网络在面对空间异质信号时仍能实现快速响应并同时保持空间信息。最后，我们进行模拟以证明我们的模型效果良好。
