| Span: Spike pattern association neuron for learning spatio-temporal spike patterns A Mohemmed, S Schliebs, S Matsuda, N Kasabov International journal of neural systems 22 (04), 1250012, 2012 | 335 | 2012 |
| Quantum-inspired evolutionary algorithm: A multimodel EDA MD Platel, S Schliebs, N Kasabov IEEE Transactions on Evolutionary Computation 13 (6), 1218-1232, 2008 | 199 | 2008 |
| Evolving spiking neural network—a survey S Schliebs, N Kasabov Evolving Systems 4, 87-98, 2013 | 193 | 2013 |
| Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models S Schliebs, M Defoin-Platel, S Worner, N Kasabov Neural Networks 22 (5-6), 623-632, 2009 | 125 | 2009 |
| Training spiking neural networks to associate spatio-temporal input–output spike patterns A Mohemmed, S Schliebs, S Matsuda, N Kasabov Neurocomputing 107, 3-10, 2013 | 116 | 2013 |
| A versatile quantum-inspired evolutionary algorithm MD Platel, S Schliebs, N Kasabov 2007 IEEE Congress on Evolutionary Computation, 423-430, 2007 | 84 | 2007 |
| On the probabilistic optimization of spiking neural networks S Schliebs, N Kasabov, M Defoin-Platel International Journal of Neural Systems 20 (06), 481-500, 2010 | 57 | 2010 |
| Method for training a spiking neuron to associate input-output spike trains A Mohemmed, S Schliebs, S Matsuda, N Kasabov International Conference on Engineering Applications of Neural Networks, 219-228, 2011 | 40 | 2011 |
| Towards spatio-temporal pattern recognition using evolving spiking neural networks S Schliebs, N Nuntalid, N Kasabov Neural Information Processing. Theory and Algorithms: 17th International …, 2010 | 28 | 2010 |
| Constructing robust liquid state machines to process highly variable data streams S Schliebs, M Fiasché, N Kasabov Artificial Neural Networks and Machine Learning–ICANN 2012: 22nd …, 2012 | 25 | 2012 |
| Bioengineering silicon quantum dot theranostics using a network analysis of metabolomic and proteomic data in cardiac ischemia F Erogbogbo, J May, M Swihart, PN Prasad, K Smart, S El Jack, D Korcyk, ... Theranostics 3 (9), 719, 2013 | 23 | 2013 |
| Are probabilistic spiking neural networks suitable for reservoir computing? S Schliebs, A Mohemmed, N Kasabov The 2011 International Joint Conference on Neural Networks, 3156-3163, 2011 | 22 | 2011 |
| Reservoir-based evolving spiking neural network for spatio-temporal pattern recognition S Schliebs, HNA Hamed, N Kasabov Neural Information Processing: 18th International Conference, ICONIP 2011 …, 2011 | 22 | 2011 |
| Computational modeling with spiking neural networks S Schliebs, N Kasabov Springer handbook of bio-/neuroinformatics, 625-646, 2014 | 18 | 2014 |
| Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms DR Pontin, S Schliebs, SP Worner, MJ Watts Ecological Modelling 222 (10), 1657-1665, 2011 | 17 | 2011 |
| SPAN: A neuron for precise-time spike pattern association A Mohemmed, S Schliebs, N Kasabov Neural Information Processing: 18th International Conference, ICONIP 2011 …, 2011 | 17 | 2011 |
| Optimization of spiking neural networks with dynamic synapses for spike sequence generation using PSO A Mohemmed, S Matsuda, S Schliebs, K Dhoble, N Kasabov The 2011 International Joint Conference on Neural Networks, 2969-2974, 2011 | 15 | 2011 |
| Quantum-inspired feature and parameter optimisation of evolving spiking neural networks with a case study from ecological modeling S Schliebs, MD Platel, S Worner, N Kasabov 2009 international joint conference on neural networks, 2833-2840, 2009 | 15 | 2009 |
| Ecological informatics for the prediction and management of invasive species SP Worner, M Gevrey, T Ikeda, G Leday, J Pitt, S Schliebs, S Soltic Springer handbook of bio-/neuroinformatics, 565-583, 2014 | 12 | 2014 |
| Evolving spike pattern association neurons and neural networks A Mohemmed, S Schliebs, S Matsuda, N Kasabov Neurocomputing 107, 3-10, 2013 | 12 | 2013 |