The nature of statistical learning theory
The nature of statistical learning theory
The dynamic universality of sigmoidal neural networks
Information and Computation
Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Isolated word recognition with the liquid state machine: a case study
Information Processing Letters - Special issue on applications of spiking neural networks
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Memory in backpropagation-decorrelation O(N) efficient online recurrent learning
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Accelerating event based simulation for multi-synapse spiking neural networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
New results on recurrent network training: unifying the algorithms and accelerating convergence
IEEE Transactions on Neural Networks
LSTM recurrent networks learn simple context-free and context-sensitive languages
IEEE Transactions on Neural Networks
A tighter bound for the echo state property
IEEE Transactions on Neural Networks
An application of recurrent nets to phone probability estimation
IEEE Transactions on Neural Networks
Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing
Neural Processing Letters
Delay learning and polychronization for reservoir computing
Neurocomputing
Improving reservoirs using intrinsic plasticity
Neurocomputing
Neuro-inspired Speech Recognition with Recurrent Spiking Neurons
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Stable Output Feedback in Reservoir Computing Using Ridge Regression
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Photonic Reservoir Computing with Coupled Semiconductor Optical Amplifiers
OSC '08 Proceedings of the 1st international workshop on Optical SuperComputing
The Separation Property Enhancement of Liquid State Machine by Particle Swarm Optimization
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
On the Quantification of Dynamics in Reservoir Computing
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Reservoir Size, Spectral Radius and Connectivity in Static Classification Problems
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Genetic algorithm for reservoir computing optimization
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Improving the separability of a reservoir facilitates learning transfer
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Benchmarking reservoir computing on time-independent classification tasks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Event detection and localization in mobile robot navigation using reservoir computing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Modular reservoir computing networks for imitation learning of multiple robot behaviors
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Towards spatio-temporal pattern recognition using evolving spiking neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Architectural and Markovian factors of echo state networks
Neural Networks
Pattern Recognition
What makes a brain smart? reservoir computing as an approach for general intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Recurrent kernel machines: Computing with infinite echo state networks
Neural Computation
The echo state conditional random field model for sequential data modeling
Expert Systems with Applications: An International Journal
Simple deterministically constructed cycle reservoirs with regular jumps
Neural Computation
A reservoir-driven non-stationary hidden Markov model
Pattern Recognition
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Neurocomputing
Neural Networks
Towards a predictive cache replacement strategy for multimedia content
Journal of Network and Computer Applications
Reservoir sizes and feedback weights interact non-linearly in echo state networks
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Regulation toward self-organized criticality in a recurrent spiking neural reservoir
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Constructing robust liquid state machines to process highly variable data streams
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
NeuCube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
An approach to reservoir computing design and training
Expert Systems with Applications: An International Journal
Oger: modular learning architectures for large-scale sequential processing
The Journal of Machine Learning Research
Modular state space of echo state network
Neurocomputing
Proceedings of the Fourth Symposium on Information and Communication Technology
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Three different uses of a recurrent neural network (RNN) as a reservoir that is not trained but instead read out by a simple external classification layer have been described in the literature: Liquid State Machines (LSMs), Echo State Networks (ESNs) and the Backpropagation Decorrelation (BPDC) learning rule. Individual descriptions of these techniques exist, but a overview is still lacking. Here, we present a series of experimental results that compares all three implementations, and draw conclusions about the relation between a broad range of reservoir parameters and network dynamics, memory, node complexity and performance on a variety of benchmark tests with different characteristics. Next, we introduce a new measure for the reservoir dynamics based on Lyapunov exponents. Unlike previous measures in the literature, this measure is dependent on the dynamics of the reservoir in response to the inputs, and in the cases we tried, it indicates an optimal value for the global scaling of the weight matrix, irrespective of the standard measures. We also describe the Reservoir Computing Toolbox that was used for these experiments, which implements all the types of Reservoir Computing and allows the easy simulation of a wide range of reservoir topologies for a number of benchmarks.