Collaboration between parallel connected neural networks -- A possible criterion for distinguishing artificial neural networks from natural organs

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.