Recurrence enhances the spatial encoding of static inputs in reservoir networks

Authors:
Christian Emmerich;René Felix Reinhart;Jochen Jakob Steil
Affiliations:
Research Institute for Cognition and Robotics, CoR-Lab, Bielefeld University, Bielefeld, Germany;Research Institute for Cognition and Robotics, CoR-Lab, Bielefeld University, Bielefeld, Germany;Research Institute for Cognition and Robotics, CoR-Lab, Bielefeld University, Bielefeld, Germany
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Year:
2010

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Abstract

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.