Tutorial - Table 1 Table 1. One is thought sufficient for most problems. More give a big increase in computational load.
Rule of thumb says the natural log of the number of inputs.
Fully interconnected feedforward net is most common. Many others exist such as adaptive resonance theory, Boltzmann machine, direct linear feedthrough, Hopfield networks, Kohonen networks. Need enough to fill parameter space and to allow generalisation. Those that do not contribute positively to discrimination may impair generalisation and are best removed by pruning the input data. Individual scaling on inputs improves learning speed dramatically.