How does a single neuron in hidden layer affect training accuracy I'm currently a student learning about AI Networks. I've came across a statement in one of my Professor's books that a FFBP (Feed-Forward Back-Propagation) Neural Network with a single hidden layer  can model any mathematic function with accuracy dependant on number of hidden layer neurons. Try as I might I cannot find any explanation as to why that occurs - could someone maybe explain the question why that is?
The claim that Neural Network with a single hidden layer can model any functions is proven in Cybenko's Approximation by superpositions of a sigmoidal function.
https://link.springer.com/article/10.1007/BF02551274
check also: https://en.wikipedia.org/wiki/Universal_approximation_theorem
The thing is that the neural network using sigmoidal functions, which are non-linear functions can.