An efficient implementation of synaptic transmission models in realistic network simulations is an important theme of computational neuro-science. The amount of CPU time required to simulate synaptic interactions can increase as the square of the number of units of such networks, depending on the connectivity convergence. As a consequence, any realistic description of synaptic phenomena, incorporating biophysical details, is computationally highly demanding. We present a consolidating algorithm based on a biophysical extended model of ligand-gated postsynaptic channels, describing short-term plasticity such as synaptic depression. The considerable speedup of simulation times makes this algorithm suitable for investigating emergent collective effects of short-term depression in large-scale networks of model neurons.

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