  The human brain is a dynamical system whose extremely complex sensor-driven
neural processes give rise to conceptual, logical cognition. Understanding the
interplay between nonlinear neural dynamics and concept-level cognition remains
a major scientific challenge. Here I propose a mechanism of neurodynamical
organization, called conceptors, which unites nonlinear dynamics with basic
principles of conceptual abstraction and logic. It becomes possible to learn,
store, abstract, focus, morph, generalize, de-noise and recognize a large
number of dynamical patterns within a single neural system; novel patterns can
be added without interfering with previously acquired ones; neural noise is
automatically filtered. Conceptors help explaining how conceptual-level
information processing emerges naturally and robustly in neural systems, and
remove a number of roadblocks in the theory and applications of recurrent
neural networks.
