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.
%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
}