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The representation of the natural-density, heterogeneous connectivity ofneuronal network models at relevant spatial scales remains a challenge forComputational Neuroscience and Neuromorphic Computing. In particular, thememory demands imposed by the vast number of synapses in brain-scale networksimulations constitutes a major obstacle. Limiting the number resolution ofsynaptic weights appears to be a natural strategy to reduce memory and computeload. In this study, we investigate the effects of a limited synaptic-weightresolution on the dynamics of recurrent spiking neuronal networks resemblinglocal cortical circuits, and develop strategies for minimizing deviations fromthe dynamics of networks with high-resolution synaptic weights. We mimic theeffect of a limited synaptic weight resolution by replacing normallydistributed synaptic weights by weights drawn from a discrete distribution, andcompare the resulting statistics characterizing firing rates, spike-trainirregularity, and correlation coefficients with the reference solution. We showthat a naive discretization of synaptic weights generally leads to a distortionof the spike-train statistics. Only if the weights are discretized such thatthe mean and the variance of the total synaptic input currents are preserved,the firing statistics remains unaffected for the types of networks consideredin this study. For networks with sufficiently heterogeneous in-degrees, thefiring statistics can be preserved even if all synaptic weights are replaced bythe mean of the weight distribution. We conclude that even for simple networkswith non-plastic neurons and synapses, a discretization of synaptic weights canlead to substantial deviations in the firing statistics, unless thediscretization is performed with care and guided by a rigorous validationprocess.

Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution