Inhibitory feedback decreases noise correlations.

A: Schematic illustration of the simulated neural network. Poisson neurons in the external population make random connections to neurons in the excitatory and inhibitory subpopulations. The connection probability P_ext ∈ [0.01, 1] is varied to achieve different levels of shared external input to the neurons. The neurons in the inhibitory (inh.) and excitatory (exc.) subpopulations make recurrent connections (exc. to exc., exc. to inh., inh. to inh., inh. to exc.) with probability P_rec = 0.2. The strength of those connections is parametrized by a_rec. B: Mean pairwise correlations between any two neurons in the exc. and inh. subpopulations plotted against the mean output of the network for different values of P_ext in a feedforward network (a_rec = 0 nS). Pairwise correlations are calculated from the number of spikes each neuron fires in a time window ΔT = 1 s across many trials of the simulation. The plot is vertically separated into two parts to also illustrate the smaller differences at lower values of P_ext. C: Mean pairwise correlations as in B, for different values of α (ratio of inhibitory-to-excitatory synaptic strength), a_rec = 0.01 nS. The black line represents the pairwise correlations in a feedforward network without any recurrent connections (a_rec = 0). D: Total current from recurrent synapses for different values of α, as in C. E-F: Same as in C-D, but with fixed α = 20 and different values of a_rec.