Deep neural networks (DNNs) are known for extracting useful information from large
amounts of data. However, the representations learned in DNNs are typically hard to
interpret, especially in dense layers. One crucial issue of the classical DNN model such as
multilayer perceptron (MLP) is that neurons in the same layer of DNNs are conditionally
independent of each other, which makes co-training and emergence of higher modularity
difficult. In contrast to DNNs, biological neurons in mammalian brains display substantial …