The GII’s first steps in the field of artificial neural networks (ANN) entailed analysing and implementing networks and efficient training algorithms for autonomous robot controllers and signal processing. Our later work was related to algorithms for the automatic generation of networks. These networks included finite state automata (FSA) or those with modifiable synaptic delays, synaptic modulation, HAN type neurons, Gaussian synapses or promoter-based units (PBGA). In more recent work, the NEAT neuroevolution algorithm has been adapted so that it can be used in problems requiring accurate temporal processing (τ-NEAT).