Spiking neural networks offer a biologically plausible account of fast neural systems. Interesting results regarding the biological relevance and computational power of these connectionist models have surfaced. However, one aspect is noticeably lacking. Little material exists describing practical computer simulations of spiking neural networks. This paper discusses the issues involved in the simulation of spiking neural networks. A number of significant issues are raised regarding the manner in which computer simulations must be performed. It is shown that simulations can only run efficiently with a substantial loss of precision. Nevertheless, it is shown that given some assumptions about the network's input and output encodings, it is possible to construct a simulator that is both efficient and accurate. Practical demonstrations of such a simulation highlight the extent to which the problems of simulation can be simplified. Despite some success with the simulator, we conclude that the...

In this paper we present a new architecture of spiking neural networks (SNNs) to control the movements of a wheelchair. In this case, to send different commands we have used electrooculography (EOG) techniques, so that, control is made by means of the ocular position (eye displacement into its orbit). Spatiotemporal coding that combines spatial constraints with temporal sequencing is of great interest to visual-like circuit model. Therefore, a neural network (SNN) is used to identify the eye model, therefore the saccadic eye movements can be detected and know where user is looking at. The system consists of a standard electric wheelchair with an on-board computer, sensors and graphical user interface running on a computer. 1.