Large-scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU).

Background: Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis.

New Method: Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology.

A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.

Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for various neural engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Compute Unified Device Architecture (CUDA) Graphics Processing Units (GPUs) can provide a low-cost, programmable, and high-performance computing platform for simulation of SNNs.