Application of constrained learning in making deep networks more transparent, regularized, and biologically plausible

Constrained learning has numerous applications and advantages, especially in circumstances in which hardware implementation imposes some constraints on us by biological justifications. Making neural network comprehensible, faster convergence and learning general properties are of other advantages of constrained learning. In this article we have tried to use constrained learning to reach more plausibility biologically. We will demonstrate that not only does the proposed model have advantages of the previously proposed models potentially, but also it can be used as a technique for regularization of neural network weights and faster convergence. Finally, having used an ensemble method of different networks, the result for MNIST dataset with data augmentation is 99.81% and the results for CIFAR-10 and SVHN datasets without data augmentation are 93.4% and 98.19% respectively.