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...