  The reason behind CNNs capability to learn high-dimensional complex features
from the images is the non-linearity introduced by the activation function.
Several advanced activation functions have been discovered to improve the
training process of neural networks, as choosing an activation function is a
crucial step in the modeling. Recent research has proposed using an oscillating
activation function to solve classification problems inspired by the human
brain cortex. This paper explores the performance of one of the CNN
architecture ALexNet on MNIST and CIFAR10 datasets using oscillatory activation
function (GCU) and some other commonly used activation functions like ReLu,
PReLu, and Mish.
