Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Fast sigmoidal networks via spiking neurons
Neural Computation
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Building silicon nervous systems with dendritic tree neuromorphs
Pulsed neural networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Learning Temporally Encoded Patterns in Networks of SpikingNeurons
Neural Processing Letters
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Neural Computation
Elman Backpropagation as Reinforcement for Simple Recurrent Networks
Neural Computation
Spike-timing error backpropagation in theta neuron networks
Neural Computation
Obstacle to training SpikeProp networks: cause of surges in training process
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A gradient descent rule for spiking neurons emitting multiple spikes
Information Processing Letters - Special issue on applications of spiking neural networks
Analysis of the ReSuMe Learning Process For Spiking Neural Networks
International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
SWAT: a spiking neural network training algorithm for classification problems
IEEE Transactions on Neural Networks
Error-backpropagation in networks of fractionally predictive spiking neurons
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Learning beyond finite memory in recurrent networks of spiking neurons
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Neural Processing Letters
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We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.