The traditional clock-driven algorithm is very time-consuming when performed on large-scale neuronal networks due to the huge number of synaptic currents computation and low performance of the parallel implementation of the algorithm. We find in this paper that the conductance coefficients of all the synapses coming from the same presynaptic neuron (neuron for example) does not need to be computed one by one, rather only one common conductance coefficient needs to be computed for all synapses from this neuron. We then propose an idea of virtual synapse for neuron to compute this common conductance coefficient and thereby have ( is the number of neurons in the network) virtual synapses for all presynaptic neurons in the network. Since each common conductance depends on only the spiking activity of the presynaptic neuron and is irrelevant of postsynaptic neurons, the computation of the different virtual synapses can be deployed to different computer processing unit efficiently. By introducing a circular data structure for the virtual synapses, we present a novel parallel clock-driven algorithm based on graphics processors for simulation of neuronal networks. It is demonstrated by test results that the proposed algorithm reduces memory and time consumption greatly, and improves the performance of the parallelization for large-scale neuronal network simulations effectively.
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