论文标题
平行连接的神经网络之间的协作 - 将人工神经网络与天然器官区分开的可能标准
Collaboration between parallel connected neural networks -- A possible criterion for distinguishing artificial neural networks from natural organs
论文作者
论文摘要
我们在实验上发现，当人工神经网络并行连接并一起训练时，它们会显示以下特性。 （i）当优化并行连接的神经网络（PNN）时，连接中的每个子网络都不会优化。 （ii）下部子网络对整个PNN的贡献与上级子网络的贡献可以与之相提并论。 （iii）即使所有子网络都给出不正确的结果，PNN也可以输出正确的结果。这些特性对于自然生物学器官不太可能。因此，它们可以作为测量神经网络仿生水平的简单但有效的标准。通过此标准，我们进一步表明，当用作激活函数时，relu函数可以使人工神经网络比sigmoid和tanh函数更具仿生性。
We find experimentally that when artificial neural networks are connected in parallel and trained together, they display the following properties. (i) When the parallel-connected neural network (PNN) is optimized, each sub-network in the connection is not optimized. (ii) The contribution of an inferior sub-network to the whole PNN can be on par with that of the superior sub-network. (iii) The PNN can output the correct result even when all sub-networks give incorrect results. These properties are unlikely for natural biological sense organs. Therefore, they could serve as a simple yet effective criterion for measuring the bionic level of neural networks. With this criterion, we further show that when serving as the activation function, the ReLU function can make an artificial neural network more bionic than the sigmoid and Tanh functions do.
