Clusters built from consumer level multiprocessor computer nodes give a room for arnbetter performance of compute intensive applications in a relatively cheaper cost asrncompared to dedicated High performance computing facilities. Simulation of biophysicalrnactivities of the organic brain using biologically realistic models is one of the areas of rncompute intensive applications that could be used on this computational platform. In thisrnwork, large scale simulation of spiking neural network(SNN) on a cluster of 8 physical rncores enabled with hyperthreading (16 logical cores) is presented. The neural network isrncomposed of the biologically plausible and computationally efficient Izhikevich singlernneuron model. To improve the performance of the simulation and effectively exploit therncomputational capacity of the cluster, we have used two parallel programmingrntechniques: distributed parallel programming using Message Passing Interface (MPI)rnlibrary and distributed shard (hybrid) parallel programming using MPI in tandem withrnOpen Multi-Processing (OpenMP) library. Moreover, to harness the combined memoryrnand computation power of the cluster the neurons were distributed across the nodesrnusing static load balancing mechanism. Hence, we were able to simulate up to 160,000rnneurons and 3.2M synapses connection per neuron. Performance evaluation for different rnconfiguration of the SNN with a purely MPI and Hybrid Parallelization method wasrnpresented. Our performance result show that for 160K neurons with 200 synapsesrnconnections, using purely MPI parallelization with 16 MPI processes the sequentialrnsimulation has improved by 43.12% and using the hybrid parallel programming the rnsequential simulation has improved by 69.58%. Hence, Comparing the performance rnresults the hybrid parallelization approach demonstrated to be a good programmingrnsolution for simulation of SNNs on a cluster of consumer level multiprocessors