Almost any process imaginable can be represented as a functional computation in a neural network, provided that the activation function is non-linear.

__https://en.wikipedia.org/wiki/Activation_function__

Non-linear functions address the problems of a linear activation function:

They allow backpropagation because they have a derivative function which is related to the inputs.

They allow “stacking” of multiple layers of neurons to create a deep neural network. Multiple hidden layers of neurons are needed to learn complex data sets with high levels of accuracy.

https://missinglink.ai/guides/neural-network-concepts/7-types-neural-network-activation-functions-right/