There is currently a strong push in the research community to develop biological 
scale implementations of neuron based vision models. Systems at this scale are 
computationally demanding and generally utilize more accurate neuron models, 
such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more 
popular integrate and fire model. We examine the feasibility of using graphics 
processing units (GPUs) to accelerate a spiking neural network based character 
recognition network to enable such large scale systems. Two versions of the 
network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. 
Three NVIDIA general-purpose (GP) GPU platforms are examined, including the 
GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that 
the GPGPUs can provide significant speedup over conventional processors. In 
particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 
5.6 and 84.4 over highly optimized implementations on the fastest central 
processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the 
Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation 
utilized all four cores and the vector data parallelism offered by the 
processor. The results indicate that GPUs are well suited for this application 
domain.