Input correlations impede suppression of chaos and learning in balanced firing-rate networks
Fig 6
Difference in chaos suppression in sparsely-connected E-I network.
λ1 as a function of I1 for common and independent inputs, showing a monotonic decrease with I1 and a larger zero-crossing for common input. This result is qualitatively similar to that obtained in the single population network with negative mean coupling (Fig 2). Error bars indicate ±2 std, lines are a guide for the eye. Increasing the excitatory efficacy α increases λ1 for both common and independent input (α ∈ {0, 0.5, 0.7}). Model parameters (parameters defined as in [16] for constant input and WI1 and WE1 are the modulation amplitudes of the input to the excitatory and inhibitory population): NE = NI = 3500, K = 700, g = 1.6, ,
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, WE1 = gαI1, WI1 = 0.44gI1, f = 0.2/τ.