Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns

Biological brains can learn, recognize, organize, and re-generate large repertoires of temporal patterns. Here I propose a mechanism of neurodynamical pattern learning and representation, called conceptors, which offers an integrated account of a number of such phenomena and functionalities. It becomes possible to store a large number of temporal patterns in a single recurrent neural network. In the recall process, stored patterns can be morphed and ”focussed”. Parametric families of patterns can be learnt from a very small number of examples. Stored temporal patterns can be content-addressed in ways that are analog to recalling static patterns in Hopfield networks.