Recurrence Enhances the Spatial Encoding of Static Inputs in Reservoir Networks

  • Christian Emmerich
  • René Felix Reinhart
  • Jochen Jakob Steil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6353)


We shed light on the key ingredients of reservoir computing and analyze the contribution of the network dynamics to the spatial encoding of inputs. Therefore, we introduce attractor-based reservoir networks for processing of static patterns and compare their performance and encoding capabilities with a related feedforward approach. We show that the network dynamics improve the nonlinear encoding of inputs in the reservoir state which can increase the task-specific performance.


Extreme Learning Machine Spectral Radius Reservoir State Echo State Network Recurrent Connection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christian Emmerich
    • 1
  • René Felix Reinhart
    • 1
  • Jochen Jakob Steil
    • 1
  1. 1.Research Institute for Cognition and Robotics (CoR-Lab)Bielefeld UniversityBielefeldGermany

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