Pulsed Neural Networks
Simulation of Spiking Neural Networks on Different Hardware Platforms
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Polychronization: Computation with Spikes
Neural Computation
Anatomy of a cortical simulator
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Communications of the ACM
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Journal of Parallel and Distributed Computing
Compass: a scalable simulator for an architecture for cognitive computing
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Artificial Neural Network Simulation on CUDA
DS-RT '12 Proceedings of the 2012 IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications
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Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Graphics Processing Units (GP Us) can provide a low-cost, programmable, and high-performance computing platform for simulation of SNNs. In thi s paper we demonstrate an efficient, Izhikevich neuron based large-scale SNN simulator that runs on a single GPU. The GPU-SNN model (running on an NVIDIA GTX-280 with 1GB of memory), is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7Hz. For simulation of 100K neurons with 10 Million synaptic connections, the GPUSNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and compact network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results were validated against CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. We intend to make our simulator available to the modeling community so that researchers will have easy access to large-scale SNN simulations.