Real-time computation at the edge of chaos in recurrent neural networks
Neural Computation
Movement Generation with Circuits of Spiking Neurons
Neural Computation
Isolated word recognition with the liquid state machine: a case study
Information Processing Letters - Special issue on applications of spiking neural networks
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
Short term memory and pattern matching with simple echo state networks
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A gradient rule for the plasticity of a neuron’s intrinsic excitability
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
New results on recurrent network training: unifying the algorithms and accelerating convergence
IEEE Transactions on Neural Networks
Delay learning and polychronization for reservoir computing
Neurocomputing
Improving reservoirs using intrinsic plasticity
Neurocomputing
Echo State Networks for Online Prediction of Movement Data --- Comparing Investigations
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Novel approaches for online playout delay prediction in VoIP applications using time series models
Computers and Electrical Engineering
Journal of Computational Neuroscience
Learning inverse kinematics for pose-constraint bi-manual movements
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Improving recurrent neural network performance using transfer entropy
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
ESN intrinsic plasticity versus reservoir stability
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Batch intrinsic plasticity for extreme learning machines
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Simple deterministically constructed cycle reservoirs with regular jumps
Neural Computation
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Echo state networks for multi-dimensional data clustering
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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We propose to use a biologically motivated learning rule based on neural intrinsic plasticity to optimize reservoirs of analog neurons. This rule is based on an information maximization principle, it is local in time and space and thus computationally efficient. We show experimentally that it can drive the neurons' output activities to approximate exponential distributions. Thereby it implements sparse codes in the reservoir. Because of its incremental nature, the intrinsic plasticity learning is well suited for joint application with the online backpropagation-decorrelation or the least mean squares reservoir learning, whose performance can be strongly improved. We further show that classical echo state regression can also benefit from reservoirs, which are pre-trained on the given input signal with the implicit plasticity rule.