Recurrence Enhances the Spatial Encoding of Static Inputs in Reservoir Networks

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. Key words: reservoir computing, extreme learning machine, static pattern recognition

• Type: Conference
• Year: 2010
• Where: ICANN
• Authors: Christian Emmerich, René Felix Reinhart, Jochen Jakob Steil
• Keywords: Attractor-based Reservoir Networks, ICANN 2010, Network Dynamics, Neural Networks, Reservoir Computing