2007 Special Issue: An experimental unification of reservoir computing methods
By David Verstraeten, Benjamin Schrauwen, Michiel D'Haene, Dirk Stroobandt. 2007
1,061 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/692bb42473af095e2264164d330533b26c32006d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Herbert Jaeger
- Mantas Lukosevicius
- Serge Massar
- Paul Dean
- Christian A. Rössert
- John Porrill
- Benjamin Schrauwen
- Fabian Triefenbach
In Cognitive Neuroscience
- Herbert Jaeger
- Christian A. Rössert
- Paul Dean
- John Porrill
- Mantas Lukosevicius
- Ali Rodan
- Peter Tino
- Mohammed Affan Zidan
In Electrical and Electronic Engineering
- Herbert Jaeger
- Mantas Lukosevicius
- Christian A. Rössert
- Paul Dean
- John Porrill
- Serge Massar
- Benjamin Schrauwen
- Kris Demuynck
Citations in the corpus
- Minimum Complexity Echo State Network (2011, methodology): “It is certainly true that, for smaller spectral radius values, the empirically estimated MC values of linear reservoirs decrease, as verified in several studies (e.g., [ 19 ]), and this may indeed be at least partially due to numerical problems in calculating higher powers of W . Moreover, empirical”
- Reservoir computing using dynamic memristors for temporal information processing (2017, background): “As can be observed from equation (1), the output y(k) at timeframe k not only depends on the current input u(k), but is also related to the cross term of past two outputs, y(k–1) and y (k–2) at timeframes k–1 and k–2, which makes it a second-order nonlinear system with a time-lag of two time-steps.”
- Design and control of compliant tensegrity robots through simulation and hardware validation (2014, neutral): “technique to train recurrent neural networks [40, 41].”
- Acoustic Modeling With Hierarchical Reservoirs (2013, background): “The term RC is a unification [21] of two similar concepts that were developed independently of each other under the names Echo State Network (ESN [20], [45]) and Liquid State Machine (LSM, [46]).”
- At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters (2015, background): “It is a measure for the exponential deviation of a system resulting from a small disturbance [25] and a value larger than 0 indicates a chaotic system.”
- A Practical Guide to Applying Echo State Networks (2012, background): “The trend started by ESNs and LSMs became lately known as Reservoir Computing (RC) [15].”
- Optoelectronic Reservoir Computing (2011, background): “Reservoir computing [2, 3, 4, 5, 6, 7, 8] provides an alternative line of attack.”