It has previously been shown that the encoding of time-dependent signals by feedforward networks (FFNs) of processing units exhibits suprathreshold stochastic resonance (SSR), which is an optimal signal transmission for a finite level of independent, individual stochasticity in the single units. In this study, a recurrent spiking network is simulated to demonstrate that SSR can be also caused by network noise in place of intrinsic noise. The level of autonomously generated fluctuations in the network can be controlled by the strength of synapses, and hence the coding fraction (our measure of information transmission) exhibits a maximum as a function of the synaptic coupling strength. The presence of a coding peak at an optimal coupling strength is robust over a wide range of individual, network, and signal parameters, although the optimal strength and peak magnitude depend on the parameter being varied. We also perform control experiments with an FFN illustrating that the optimized coding fraction is due to the change in noise level and not from other effects entailed when changing the coupling strength. These results also indicate that the non-white (temporally correlated) network noise in general provides an extra boost to encoding performance compared to the FFN driven by intrinsic white noise fluctuations.

先前已经表明，处理单元的前馈网络 (FFN) 对时间相关信号的编码表现出超阈值随机共振 (SSR)，这是单个单元中有限水平的独立、个体随机性的最佳信号传输。在这项研究中，模拟了一个循环尖峰网络，以证明 SSR 也可能是由网络噪声而不是固有噪声引起的。网络中自主产生的波动水平可以通过突触的强度来控制，因此编码分数（我们对信息传输的测量）表现出作为突触耦合强度的函数的最大值。最佳耦合强度处的编码峰值在广泛的个体、网络和信号参数范围内是稳健的，尽管最佳强度和峰值幅度取决于变化的参数。我们还使用 FFN 进行了控制实验，说明优化的编码分数是由于噪声水平的变化，而不是由于改变耦合强度时产生的其他影响。这些结果还表明，与由固有白噪声波动驱动的 FFN 相比，非白（时间相关）网络噪声通常为编码性能提供了额外的提升。