A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components. We focus on conductance-based models for neurons that emulate the temporal dynamics of the synaptic integration process. We have designed an efficient computing architecture using reconfigurable hardware in which the different stages of the neuron model are processed in parallel (using a customized pipeline structure). Further improvements occur by computing multiple neurons in parallel using multiple processing units. The computing platform is described and its scalability and performance evaluated. The goal is to investigate biologically realistic models for the control of robots operating within closed perception-action loops.