Learning and retrieval in attractor neural networks above saturation
Meir Griniasty, Hanoch Gutfreund · Journal of Physics A Mathematical and General · 1991
The authors investigate neural networks in the range of parameters when the ground-state energy is positive; namely, when a synaptic matrix which satisfies all the desired constraints cannot be found by the learning algorithm. In particular, they calculate the typical distribution functions of local stabilities obtained for a number of algorithms in this region. These functions are used to investigate the retrieval properties as reflected by the size of the basins of attraction. This is done analytically in sparsely connected networks, and numerically in fully connected networks. The main conclusion is that the retrieval behaviour of attractor neural networks can be improved by learning above saturation.