Figure 1.

Model illustrating effects of noise correlations on the variability of synaptic current and spike output. Neural encoding consists of three basic steps: a stimulus shapes excitatory (blue: G_exc) and inhibitory (red: G_inh) synaptic conductances; these conductances then shape synaptic currents; and the resulting currents control spike generation to produce a sequence of action potentials (spikes). Noise correlations will be strong if a common source dominates noise in excitatory and inhibitory pathways (Noise_com) and minimal if the dominant noise source arises independently (Noise_ind). Correlated (black traces) as opposed to uncorrelated (green traces) noise between excitatory and inhibitory conductances can lead to lower variability of both the synaptic current and spike output (shaded regions around traces). Understanding this issue requires answering two questions: ➀ How much do converging excitatory and inhibitory input covary? ➁ What is the impact of such noise correlations on the neural output?