Autoassociative memory with high storage capacity

Abstract

The general neural unit (GNU) [1] is known for its high storage capacity as an autoassociative memory. The exponential increase in its storage capacity with the number of inputs per neuron is far greater than the linear growth in the famous Hopfield network [2]. This paper shows that the GNU attains an even higher capacity with the use of pyramids of neurons instead of single neurons as its nodes. The paper also shows that the storage capacity/cost ratio increases, giving further support to this node upgrade. This analysis combines the modular approach for storage capacity assessment of pyramids [3] and of GNUs [4].