A novel GPU-based simulation of spiking neural networks is implemented as a hybrid system using ParkerSochacki numerical integration method with adaptive order. Full single-precision floating-point accuracy for all model variables is achieved. The implementation is validated with exact matching of all neuron potential traces from GPU-based simulation versus those of a reference CPU-based simulation. A network of 4096 Izhikevich neurons simulated on an NVIDIA GTX260 device achieves real-time performance with a speedup of 9 compared to simulation executed on Opteron 285, 2.6-GHz device.Index Terms-GPU, CUDA, STDP, spiking neural network, high accuracy, parallel computing, shared memory.
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