Introduction of a Hebbian unsupervised learning algorithm to boost the encoding capacity of Hopfield networks

The learning impact, of an iterative supervised Hebbian learning algorithm, on a recurrent neural network\´s underlying dynamics has been discussed in a previous paper. It was argued that these results are in line with the observations made by Freeman in the olfactory bulb of the rabbit: cycles are used to store information and the chaotic dynamics appears as the background regime composed of those cyclic "memory bags". However, to get closer to a biological point of view, this paper introduces an unsupervised version of this Hebbian algorithm. As a direct result, both the storing capacity and the content addressability of the learned networks are greatly enhanced. Furthermore, stunning dynamical results are observed: if the learning process increases the dimension of the potential attractors, however, less chaoticity is found than in a supervised learning process. Moreover, chaos obtained looks more structured, made from brief itinerancy among learned cycles.