2007 Special Issue: Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning
By Jochen J. Steil. 2007
189 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering
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
- J. Urgen Schmidhuber
- Jan Peters
- Sergey Levine
- Pieter Abbeel
- Yoshua Bengio
- Duy Nguyen-Tuong
- Chelsea Finn
- Bernhard Schölkopf
In Cognitive Neuroscience
- Duy Nguyen-Tuong
- Ali Rodan
- Peter Tino
- Benjamin Schrauwen
- Jan Peters
- J. Urgen Schmidhuber
- Seiji Takeda
- Gouhei Tanaka
In Electrical and Electronic Engineering
- J. Urgen Schmidhuber
- Ali Rodan
- Peter Tino
- Benjamin Schrauwen
- Duy Nguyen-Tuong
- Jan Peters
- Seiji Takeda
- Gouhei Tanaka
Citations in the corpus
- Deep learning in neural networks: An overview (2014, methodology): “Compare Section 5.10 on UL-based RNN stacks (1991), as well as later UL RNNs (e.g., Klapper-Rybicka, Schraudolph, & Schmidhuber, 2001; Steil, 2007).”
- SORN: A Self-Organizing Recurrent Neural Network (2009, unknown): “In a RNN, IP induced robust homeostatic effects on the network dynamics (Steil, 2007; Schrauwen et al., 2008).”
- Minimum Complexity Echo State Network (2011, methodology): “We use a range of timeseries covering a wide spectrum of memory structure and widely used in the ESN literature [3], [4], [6], [10], [11], [19]–[21].”
- An overview of reservoir computing: theory, applications and implementations (2007, methodology): “These type of learning rules are called Intrinsic Plasticity, originally discovered in real neurons [64], derived for analog neurons by Triesch [56], and first used in RC by Steil [52].”
- Recent Advances in Physical Reservoir Computing: A Review (2018, background): “The backpropagation decorrelation (BPDC) learning rule (Steil, 2004, 2007) for RNNs is also regarded as a predecessor of RC. Similar concepts and models in special cases were reported in earlier studies as summarized in Jaeger (2007), including sequential associative memory models (Gallant & King,…”
- Model learning for robot control: a survey (2011, background): “2007; Lukocevicius and Jaeger 2009; Reinhart and Steil 2009) and echo state neural networks (Steil 2007; Joshi and Maass 2005; Rolf et al. 2010).”