an efficient simulation environment for modeling large-scale cortical processing
Clicks: 174
ID: 179596
2011
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 (STDP) and Short-Term Plasticity (STP). 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.
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erichert2011frontiersan
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| Authors | ;Micah eRichert;Jayram Moorkanikara Nageswaran;Nikil eDutt;Jeffrey L Krichmar |
| Journal | Nucleic Acids Research |
| Year | 2011 |
| DOI | 10.3389/fninf.2011.00019 |
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