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.
Unable to display preview. Download preview PDF.
- 2.Jaeger, H.: The echo state approach to analysing and training recurrent neural networks. Technical Report 148, German National Research Center for Information Technology (2001)Google Scholar
- 3.Verstraeten, D., Schrauwen, B., Stroobandt, D.: Reservoir-based techniques for speech recognition. In: Proc. IEEE IJCNN, pp. 1050–1053 (2006)Google Scholar
- 6.Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
- 7.Forina, M., Armanino, C.: Eigenvector projection and simplified nonlinear mapping of fatty acid content of italian olive oils. Ann. Chem. (72), 125–127 (1982)Google Scholar