An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing

Micah Richert, Jayram Moorkanikara Nageswaran, Nikil Dutt, Jeffrey L. Krichmar
Department of Cognitive Sciences, University of California, Irvine, CA, US
Frontiers in Neuroinformatics, 5: 19, 2011


   title={An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing},

   author={Richert, M. and Nageswaran, J.M. and Dutt, N. and Krichmar, J.L.},

   journal={Frontiers in Neuroinformatics},



   publisher={Frontiers Media SA}


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.
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