  An activation function has a significant impact on the efficiency and
robustness of the neural networks. As an alternative, we evolved a cutting-edge
non-monotonic activation function, Negative Stimulated Hybrid Activation
Function (Nish). It acts as a Rectified Linear Unit (ReLU) function for the
positive region and a sinus-sigmoidal function for the negative region. In
other words, it incorporates a sigmoid and a sine function and gaining new
dynamics over classical ReLU. We analyzed the consistency of the Nish for
different combinations of essential networks and most common activation
functions using on several most popular benchmarks. From the experimental
results, we reported that the accuracy rates achieved by the Nish is slightly
better than compared to the Mish in classification.
