The effects of network topologies on signal propagation are studied in noisy feedforward neural network in detail, where the network topologies are modulated by changing both the in-degree and out-degree distributions of FFNs as identical, uniform and exponential respectively. Stochastic resonance appeared in three FFNs when the same external stimuli and noise are applied to the three different network topologies. It is found that optimal noise intensity decreases with the increase of network’s layer index. Meanwhile, the Q index of FFN with identical distribution is higher than that of the other two FFNs, indicating that the synchronization between the neuronal firing activities and the external stimuli is more obvious in FFN with identical distribution. The optimal parameter regions for the time cycle of external stimuli and the noise intensity are found for three FFNs, in which the resonance is more easily induced when the parameters of stimuli are set in this region. Furthermore, the relationship between the in-degree, the average membrane potential and the resonance performance is studied at the neuronal level, where it is found that both the average membrane potentials and the Q indexes of neurons in FFN with identical degree distribution is more consistent with each other than that of the other two FFNs due to their network topologies. In summary, the simulations here indicate that the network topologies play essential roles in affecting the signal propagation of FFNs.

中文翻译：

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网络拓扑对前馈神经网络中随机共振的影响。

在嘈杂的前馈神经网络中详细研究了网络拓扑结构对信号传播的影响，在该网络中，通过将FFN的入度和出度分布分别更改为相同，均匀和指数来调制网络拓扑。当将相同的外部刺激和噪声应用于三种不同的网络拓扑结构时，三个FFN中会出现随机共振。发现最佳噪声强度随网络层指数的增加而降低。同时，Q具有相同分布的FFN指数高于其他两个FFN，表明在具有相同分布的FFN中，神经元放电活动与外部刺激之间的同步性更为明显。对于三个FFN，找到了外部刺激时间周期的最佳参数区域和噪声强度，其中在该区域设置刺激参数时，更容易引起共振。此外，在神经元水平研究了度数，平均膜电位和共振性能之间的关系，发现平均膜电位和Q具有相同程度分布的FFN中神经元的索引比其他两个FFN的神经网络索引彼此更一致。总之，此处的仿真表明网络拓扑在影响FFN的信号传播方面起着至关重要的作用。