If a neural network has a limited number of neuron parameters to find, -let's say only 1000 parameters-, it is generally better to spend the parameters on weights or neuron bias?
For example, if each neuron has 2 weights and one bias, it uses 3 parameters per neuron, so only 333 neurons would be available.
But if each neuron uses no bias parameter, then 500 neurons are available with 1000 parameters.
I'm concerned with overfiting by using too many parameters, so I want to minimize the number of parameters meanwhile maximizing the quality of the result.