Synchrony and desynchrony in integrate-and-fire oscillators
Neural Computation
Image Segmentation by Networks of Spiking Neurons
Neural Computation
A data parallel approach to genetic programming using programmable graphics hardware
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Using parallel GPU architecture for simulation of planar I/F networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Efficient simulation of large-scale spiking neural networks using CUDA graphics processors
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Fuzzy ART neural network parallel computing on the GPU
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Simulating biological-inspired spiking neural networks with OpenCL
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Fuzzy ARTMAP based neural networks on the GPU for high-performance pattern recognition
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
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Simulating large networks of spiking neurons is a very common task in the areas of Neuroinformatics and Computational Neurosciences. These simulations are time-consuming but also often intrinsically parallel. The recent advent of powerful and programmable graphic cards seems to be a pertinent solution to the problem: they offer a cheap but efficient possibility to serve as very fast co-processors for the parallel computing that spiking neural networks need. We describe our implementation of three different problems on such a card: two image-segmentation algorithms using spiking neural networks and one multi-purpose spiking neural-network simulator. Using these examples we show the benefits, the challenges and the limits of such an implementation.