In the previous chapter we arrived at the conclusion that McCulloch—Pitts units can be used to build networks capable of computing any logical function and of simulating any finite automaton. From the biological point of view, however, the types of network that can be built are not very relevant. The computing units are too similar to conventional logic gates and the network must be completely specified before it can be used. There are no free parameters which could be adjusted to suit different problems. Learning can only be implemented by modifying the connection pattern of the network and the thresholds of the units, but this is necessarily more complex than just adjusting numerical parameters. For that reason, we turn our attention to weighted networks and consider their most relevant properties. In the last section of this chapter we show that simple weighted networks can provide a computational model for regular neuronal structures in the nervous system.