On the complexity of learning for a spiking neuron (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
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
On the complexity of learning for spiking neurons with temporal coding
Information and Computation
On the relevance of time in neural computation and learning
Theoretical Computer Science
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
On computing Boolean functions by a spiking neuron
Annals of Mathematics and Artificial Intelligence
Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Polychronization: Computation with Spikes
Neural Computation
On the Nonlearnability of a Single Spiking Neuron
Neural Computation
Learning sensory representations with intrinsic plasticity
Neurocomputing
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
Algorithms for Structural and Dynamical Polychronous Groups Detection
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Benchmarking reservoir computing on time-independent classification tasks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Supervised associative learning in spiking neural network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Evaluating the effect of spiking network parameters on polychronization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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We propose a multi-timescale learning rule for spiking neuron networks, in the line of the recently emerging field of reservoir computing. The reservoir is a network model of spiking neurons, with random topology and driven by STDP (spike-time-dependent plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that affects the synaptic delays linking the network to the readout neurons, with classification as a goal task. The network processing and the resulting performance can be explained by the concept of polychronization, proposed by Izhikevich [Polychronization: computation with spikes, Neural Comput. 18(2) (2006) 245-282], on physiological grounds. The model emphasizes that polychronization can be used as a tool for exploiting the computational power of synaptic delays and for monitoring the topology and activity of a spiking neuron network.