@techreport{Jaeger2014,
  type      = {Working Paper},
  author    = {Jaeger, Herbert},
  title     = {Controlling Recurrent Neural Networks by Conceptors},
  url       = {https://nbn-resolving.org/urn:nbn:de:gbv:579-opus-1006250},
  series    = {Constructor University Technical Reports},
  number    = {31},
  year      = {2014},
  abstract  = {The human brain is a dynamical system whose extremely complex sensordriven 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.},
  language  = {en}
}