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
Neural networks with dynamic synapses
Neural Computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
The evidence for neural information processing with precise spike-times: A survey
Natural Computing: an international journal
Advances in Design and Application of Spiking Neural Networks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Fuzzy-neural computation and robotics
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Corrections to "Pareto-based multiobjective machine learning: An overview and case studies"
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A gradient descent rule for spiking neurons emitting multiple spikes
Information Processing Letters - Special issue on applications of spiking neural networks
An adaptive multiobjective approach to evolving ART architectures
IEEE Transactions on Neural Networks
Optimization methods for spiking neurons and networks
IEEE Transactions on Neural Networks
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
Multiobjective learning in the random neural network
International Journal of Advanced Intelligence Paradigms
| Hi-index | 0.00 |
Evolutionary multi-objective optimization of spiking neural networks for solving classification problems is studied in this paper. By means of a Paretobased multi-objective genetic algorithm, we are able to optimize both classification performance and connectivity of spiking neural networks with the latency coding. During optimization, the connectivity between two neurons, i.e., whether two neurons are connected, and if connected, both weight and delay between the two neurons, are evolved. We minimize the the classification error in percentage or the root mean square error for optimizing performance, and minimize the number of connections or the sum of delays for connectivity to investigate the influence of the objectives on the performance and connectivity of spiking neural networks. Simulation results on two benchmarks show that Pareto-based evolutionary optimization of spiking neural networks is able to offer a deeper insight into the properties of the spiking neural networks and the problem at hand.