Input correlations impede suppression of chaos and learning in balanced firing-rate networks

doi:10.1371/journal.pcbi.1010590

Input correlations impede suppression of chaos and learning in balanced firing-rate networks

Fig 2

Largest Lyapunov exponent shows different chaos suppression for common vs independent input.

Largest Lyapunov exponent λ₁ as a function of input modulation amplitude I₁ for common (green) and independent (violet) input. are the zero-crossings of λ₁ and thus the minimum I₁ required to suppress chaotic dynamics. With common input, λ₁ crosses zero at a much larger I₁. Dots with error bars are numerical simulations, dashed lines are largest Lyapunov exponents computed by dynamic mean-field theory (DMFT). Error bars indicate ±2 std across 10 network realizations. Model parameters: N = 5000, g = 2, f = 0.2/τ, I₀ = J₀ = 1.

doi: https://doi.org/10.1371/journal.pcbi.1010590.g002