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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Comparing Metaheuristic Algorithms on the Training Process of Spiking Neural Networks
verfasst von : Andrés Espinal, Martín Carpio, Manuel Ornelas, Héctor Puga, Patricia Melin, Marco Sotelo-Figueroa
Erschienen in: Recent Advances on Hybrid Approaches for Designing Intelligent Systems
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Abstract
Spiking Neural Networks are considered as the third generation of Artificial Neural Networks. In these networks, spiking neurons receive/send the information by timing of events (spikes) instead by the spike rate; as their predecessors do. Spikeprop algorithm, based on gradient descent, was developed as learning rule for training SNNs to solve pattern recognition problems; however this algorithm trends to be trapped in local minima and has several limitations. For dealing with the supervised learning on Spiking Neural Networks without the drawbacks of Spikeprop, several metaheuristics such as: Evolutionary Strategy, Particle Swarm Optimization, have been used to tune the neural parameters. This work compares the performance and the impact of some metaheuristics used for training spiking neural networks.