> Biases are just weights on an always on input. Granted, however this approach does not require that constant-one input either. > There isn't much difference

> Biases are just weights on an always on input. Granted, however this approach does not require that constant-one input either. > There isn't much difference between weights of a linear sum and coefficients of a function. Yes, the trained function coefficients of this approach are the equivalent to the trained weights of MLP. Still this approach does not require the globally uniform activation function of MLP.