Is better to spend parameters on weights or bias? 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.
First of all, your estimates are a bit off. If you have 300 neurons, you won't have just 2 weights per neuron, but much more, assuming full connectivity

Bias isn't just an extra parameter to fit, it is an important adjustable parameter that sets the offset of the separating hyperplane represented by each neuron. Think of the simple equation $ax+b$, there's no way to shift the line unless you use the $b$ (bias) part.

This would be especially important for small number of nodes and classification tasks (think perceptrons etc)