Dynamic stochastic synapses as computational units
Neural Computation
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Computational Neurogenetic Modeling
Computational Neurogenetic Modeling
Natural Computing: an international journal
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
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
Letter to the editor: Neural networks including microRNAs
Neural Networks
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
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
A target-reaching controller for mobile robots using spiking neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Computational Intelligence and Neuroscience
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Spiking neural networks (SNN) are promising artificial neural network (ANN) models as they utilise information representation as trains of spikes, that adds new dimensions of time, frequency and phase to the structure and the functionality of ANN. The current SNN models though are deterministic, that restricts their applications for large scale engineering and cognitive modelling of stochastic processes. This paper proposes a novel probabilistic spiking neuron model (pSNM) and suggests ways of building pSNN for a wide range of applications including classification, string pattern recognition and associative memory. It also extends previously published computational neurogenetic models.