It is easily amenable to mathematical analysis, but is not in itself suitable for either detailed neural modeling or practical computational intelligence. This article reports some experiments on Hopfield net dynamics that were done with a Webmind implementation in mind. The results given here demonstrate quite clearly that, by introducing a cap on the absolute value of link weights, even a very sparsely connected Hopfield net can be made to function effectively as a continuous learning system. The catch, however, is that for low connectivity, the window of learning is very small, so that a large number of neurons are required to maintain a small, continuously shifting window of memories. The training is done as follows.
All weights start at zero.
The training patterns c j are cycled through one after the other. Each training pattern, in its turn, causes those nodes that it contains to be activated. Once this has been done, the training patterns can be retrieved from the network as follows. Stimulate some nodes, and let activation spread. Note that steady convergence to an attractor is only guaranteed if the weights w i,j are symmetric.