%0 Conference Paper %1 fidjeland_accelerated_2010 %A Fidjeland, A.K. %A Shanahan, M.P. %B Neural Networks (IJCNN), The 2010 International Joint Conference on %D 2010 %K cuda gpu simulation %P 1--8 %R 10.1109/IJCNN.2010.5596678 %T Accelerated simulation of spiking neural networks using GPUs %X \textlessp\textgreaterSpiking neural network simulators provide environments in which to implement and experiment with models of biological brain structures. Simulating large-scale models is computationally expensive, however, due to the number and interconnectedness of neurons in the brain. Furthermore, where such simulations are used in an embodied setting, the simulation must be real-time in order to be useful. In this paper we present a platform (nemo) for such simulations which achieves high performance on parallel commodity hardware in the form of graphics processing units (GPUs). This work makes use of the Izhikevich neuron model which provides a range of realistic spiking dyn28amics while being computationally efficient. Learning is facilitated through spike-timing dependent synaptic plasticity. Our GPU kernel can deliver up to 550 million spikes per second using a single device. This corresponds to a real-time simulation of around 55 000 neurons under biologically plausible conditions with 1000 synapses per neuron and a mean firing rate of 10 Hz.\textless/p\textgreater %@ 1098-7576

@inproceedings{fidjeland_accelerated_2010, abstract = {{\textless}p{\textgreater}Spiking neural network simulators provide environments in which to implement and experiment with models of biological brain structures. Simulating large-scale models is computationally expensive, however, due to the number and interconnectedness of neurons in the brain. Furthermore, where such simulations are used in an embodied setting, the simulation must be real-time in order to be useful. In this paper we present a platform (nemo) for such simulations which achieves high performance on parallel commodity hardware in the form of graphics processing units ({GPUs).} This work makes use of the Izhikevich neuron model which provides a range of realistic spiking dyn28amics while being computationally efficient. Learning is facilitated through spike-timing dependent synaptic plasticity. Our {GPU} kernel can deliver up to 550 million spikes per second using a single device. This corresponds to a real-time simulation of around 55 000 neurons under biologically plausible conditions with 1000 synapses per neuron and a mean firing rate of 10 Hz.{\textless}/p{\textgreater}}, added-at = {2014-01-19T04:44:12.000+0100}, author = {Fidjeland, {A.K.} and Shanahan, {M.P.}}, bdsk-url-1 = {http://dx.doi.org/10.1109/IJCNN.2010.5596678}, biburl = {https://www.bibsonomy.org/bibtex/2d68ba0c4f7e85326e49e1dd7919364af/neurokernel}, booktitle = {Neural Networks ({IJCNN)}, The 2010 International Joint Conference on}, doi = {10.1109/IJCNN.2010.5596678}, interhash = {784b8899cc5fc32e1ade59899501a490}, intrahash = {d68ba0c4f7e85326e49e1dd7919364af}, isbn = {1098-7576}, keywords = {cuda gpu simulation}, pages = {1--8}, timestamp = {2014-01-19T04:44:12.000+0100}, title = {Accelerated simulation of spiking neural networks using {GPUs}}, year = 2010 }