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.
KeywordsExtreme Learning Machine Spectral Radius Reservoir State Echo State Network Recurrent Connection
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