Supervised classification with a binary associative memory

The authors present the application of a binary associative memory to classification tasks together with a memory efficient programming algorithm which is fast, even in simulations on a sequential computer. The associative memory is a neural network like structure with binary synaptic weights. Due to its simplicity it is rather simple to analyse compared with other more sophisticated neural networks. Furthermore, the memory is easily realisable by means of dedicated VLSI chips up to a size of several thousand neurons and a storing capacity of several megabyte. The application pursued is the control of an autonomous vehicle. >