< Back to Article

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

Fig 4

Mechanism of chaos suppression with slowly varying common input.

A) External input (dashed) and recurrent input (solid) for three example neurons. B) Synaptic currents h_i for four example neurons. C) Local Lyapunov exponent from network simulation, which reflects the local exponential growth rates between nearby trajectories (solid), and Lyapunov exponent from stationary DMFT (dashed) used in quasi-static approximation. When , external input periodically becomes negative and silences the recurrent activity (gray bars). During these silent episodes, the network is no longer chaotic and . When the input is positive, dynamics remains chaotic and on average. Model parameters: N = 5000, g = 2, f = 0.01/τ, I₀ = J₀ = 1.

doi: https://doi.org/10.1371/journal.pcbi.1010590.g004