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An efficient simulation environment for modeling large-scale cortical processing.

Micah Richert¹, Jayram Moorkanikara Nageswaran, Nikil Dutt

¹Department of Cognitive Sciences, University of California Irvine, CA, USA.

Frontiers in Neuroinformatics

|October 19, 2011

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We created a user-friendly, efficient spiking neural network simulator for large-scale computational neuroscience models. This tool enables real-time simulation of complex brain networks on GPUs, advancing neuroscience research.

Area of Science:

Computational Neuroscience
Artificial Neural Networks
Neuroscience Simulation

Background:

Large-scale computational neuroscience models are crucial for understanding brain function.
Existing simulators often face challenges in usability and computational efficiency.
Developing efficient tools is essential for advancing complex neural network simulations.

Purpose of the Study:

To develop a user-friendly and computationally efficient spiking neural network simulator.
To facilitate the generation of large-scale computational neuroscience models.
To enable real-time simulations of complex neural systems.

Main Methods:

Developed a C/C++ based spiking neural network simulator.
Implemented Izhikevich neuron networks with plasticity mechanisms (STDP, STP).

Keywords:

GPU STDP computational neuroscience short-term plasticity simulation software spiking neurons visual cortex

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Enabled execution on GPUs and CPUs with a standard network construction interface.

Main Results:

Demonstrated ease of use and computational efficiency with a large-scale cortical model (V1, V4, MT).
The model comprised 138,240 neurons and ~30 million synapses.
Achieved real-time simulation performance on an off-the-shelf GPU.

Conclusions:

The developed simulator is effective for creating and running large-scale neural network models.
The tool offers significant computational efficiency, enabling real-time simulations.
Publicly available source code promotes accessibility and further development in computational neuroscience.