Fig. 6

Firing rate variance ν vs. synaptic strength g in an excitatory recurrent network. Equation (19) is plotted with \(\eta^{2} = 5\) and \(C = 1\). When synaptic strength is zero, all firing rate variance is due to noise, so \(\nu= \frac{\eta^{2}}{2}\). As synaptic strength increases, firing rate variance increases. As synaptic strength approaches unity, recurrent excitation acts to reinforce variations in firing rate, and variance asymptotes to ∞. If target firing rates are set such that the characteristic firing rate variance \(\nu^{*}\) is large, then the synaptic strength \(g^{*}\) at a control system fixed point must be close to unity, making the network an integrator of its inputs