Artificial neural networks are a key tool for researchers attemptingto understand and replicate the behaviour and intelligencefound in biological neural networks. Software simulations offergreat flexibility and the ability to select which aspects of biologicalnetworks to model, but are slow when operating on more complexbiologically plausible models; while dedicated hardware solutions canbe very fast, they are restricted to fixed models. This paperuses FPGAs to achieve a compromise between model complexity and simulationspeed, such that a fully-connected network of 1024 neurons,based on the biologically plausible Izhikevich spiking model,can be simulated at 100 times real-time speed. The simulatoris based on a re-usable interconnection architecture for storing synapse weights andcalculating thalamic input, which makes use of the large number of available block-RAMsand huge amounts of fine-grain parallelism. The simulatorachieves a sustained throughput of 2.26 GFlops in double-precision, and a single Virtex-5 xc5vlx330t without off-chip storage running at 133MHzis 16 times faster than a 3GHz Core2 CPU, and 1.1 times faster thana single-precision 1.2GHz 30-core GPU.