LETTER Communicated by Walter Senn Spike-Timing-Dependent Plasticity in Balanced Random Networks

The balanced random network model attracts considerable interest be-cause it explains the irregular spiking activity at low rates and largemem-brane potential fluctuations exhibited by cortical neurons in vivo. In this article, we investigate to what extent this model is also compatible with the experimentally observed phenomenon of spike-timing-dependent plasticity (STDP). Confrontedwith the plethora of theoreticalmodels for STDP available, we reexamine the experimental data. On this basis, we propose a novel STDP update rule, with a multiplicative dependence on the synaptic weight for depression, and a power law dependence for potentiation. We show that this rule, when implemented in large, balanced networks of re-alistic connectivity and sparseness, is compatible with the asynchronous irregular activity regime. The resultant equilibrium weight distribution is unimodal with fluctuating individual weight trajectories and does not exhibit development of structure. We investigate the robustness of our results with respect to the relative strength of depression. We introduce synchronous stimulation to a group of neurons and demonstrate that the decoupling of this group from the rest of the net-work is so severe that it cannot effectively control the spiking of other neurons, even those with the highest convergence from this group