Controlling Recurrent Neural Networks by Conceptors

  • 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.

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Publishing Institution:IRC-Library, Information Resource Center der Jacobs University Bremen
Granting Institution:Jacobs Univ.
Author:Herbert Jaeger
Persistent Identifier (URN):urn:nbn:de:gbv:579-opus-1006250
Alternate Publication on arXiv.org:http://arxiv.org/abs/1403.3369
Series (No.):Constructor University Technical Reports (31)
Document Type:Technical Report
Language:English
Date of First Publication:2014/03/01
School:SES School of Engineering and Science
Library of Congress Classification:Q Science / QA Mathematics (incl. computer science) / QA71-90 Instruments and machines / QA75.5-76.95 Electronic computers. Computer science / QA76.87 Neural computers. Neural networks

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