The Road To Chaos By Time-Asymmetric Hebbian Learning, Neural Comp.
This letter aims at studying the impact of iterative Hebbian learning algorithms on the recurrent neural network's underlying dynamics. (...) The impact of the learning on the network's dynamics is the following: the more information to be stored as limit cycle attractors of the neural network, the more chaos prevails as the background dynamical regime of the network. In fact, the background chaos spreads widely and adopts a very unstructured shape similar to white noise. Next, we introduce a new form of supervised learning that is more plausible from a biological point of view (...).