  A resistive memory network that has no crossover wiring is proposed to
overcome the hardware limitations to size and functional complexity that is
associated with conventional analogue neural networks. The proposed memory
network is based on simple network cells that are arranged in a hierarchical
modular architecture. Cognitive functionality of this network is demonstrated
by an example of character recognition. The network is trained by an
evolutionary process to completely recognise characters deformed by random
noise, rotation, scaling and shifting
