%0 Journal Article %1 richert_efficient_2011 %A Richert, Micah %A Nageswaran, Jayram Moorkanikara %A Dutt, Nikil %A Krichmar, Jeffrey L. %D 2011 %J Frontiers in Neuroinformatics %K gpu simulation %P 19 %R 10.3389/fninf.2011.00019 %T An efficient simulation environment for modeling large-scale cortical processing %U http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2011.00019/pdf/full %V 5 %X We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available.

@article{richert_efficient_2011, abstract = {We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either {GPUs} or {CPUs.} The simulator, which is written in {C/C++}, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area {MT.} The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf {GPU.} The simulator source code, as well as the source code for the cortical model examples is publicly available.}, added-at = {2014-01-19T08:31:17.000+0100}, author = {Richert, Micah and Nageswaran, Jayram Moorkanikara and Dutt, Nikil and Krichmar, Jeffrey L.}, bdsk-url-1 = {http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2011.00019/pdf/full}, bdsk-url-2 = {http://dx.doi.org/10.3389/fninf.2011.00019}, biburl = {https://www.bibsonomy.org/bibtex/2f4a6860bcffe5d8c8473ece8004568c1/neurokernel}, doi = {10.3389/fninf.2011.00019}, interhash = {7c27021e02c42bdb667617981fe783d8}, intrahash = {f4a6860bcffe5d8c8473ece8004568c1}, journal = {Frontiers in Neuroinformatics}, keywords = {gpu simulation}, pages = 19, timestamp = {2014-01-19T08:31:17.000+0100}, title = {An efficient simulation environment for modeling large-scale cortical processing}, url = {http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2011.00019/pdf/full}, urldate = {2011-09-23}, volume = 5, year = 2011 }