GPU-based simulation of brain neuron models

The human brain is an incredible system which can process, store, and transfer information with high speed and volume. Inspired by such system, engineers and scientists are cooperating to construct a digital brain with these characteristics. The brain is composed by billions of neurons which can be modeled by mathematical equations. The first step to reach that goal is to be able to construct these neuron models in real time. The Inferior Olive (IO) model is a selected model to achieve the real time simulation of a large neuron network. The model is quite complex with three compartments which are based on the Hodgkin Huxley model. Although the Hodgkin Huxley model is considered as the most biological plausible model, it has quite high complexity. The three compartments also make the model become even more computationally intensive. A CPU platform takes a long time to simulate such a complex model. Besides, FPGA platform does not handle effectively floating point operations. With GPU's capability of high performance computing and floating point operations, GPU platform promises to facilitate computational intensive applications successfully. In this thesis, two GPU platforms of the two latest Nvidia GPU architectures are used to simulate the IO model in a network setting. The performance is improved significantly on both platforms in comparison with that on the CPU platform. The speed-up of double precision simulation is 68.1 and 21.0 on Tesla C2075 and GeForce GT640, respectively. The single precision simulation is nearly twice faster than the double precision simulation. The performance of the GeForce GT640 platform is 67% less than that on the Tesla C2075 platfom, while the cost efficiency on the GeForce GT640 is eight times higher than that on the Tesla C2075 platform. The real time execution is achieved with approximately 256 neural cells. In conclusion, the Tesla C2075 platform is essential for double precision simulation and the GeForce GT640 platform is more suitable for reducing execution time of single precision simulation.Computer EngineeringMicroelectronics & Computer EngineeringElectrical Engineering, Mathematics and Computer Scienc