Stable chaos in fluctuation driven neural circuits

Abstract

We study the dynamical stability of pulse coupled networks of leaky integrate-and-fire neurons against infinitesimal and finite perturbations. In particular, we compare current versus fluctuations driven networks, the former (latter) is realized by considering purely excitatory (inhibitory) sparse neural circuits. In the excitatory case the instabilities of the system can be completely captured by an usual linear stability (Lyapunov) analysis, on the other hand the inhibitory networks can display the coexistence of linear and nonlinear instabilities. The nonlinear effects are associated to finite amplitude instabilities, which have been characterized in terms of suitable indicators. For inhibitory coupling one observes a transition from chaotic to non chaotic dynamics by decreasing the pulse width. For sufficiently fast synapses the system, despite showing an erratic evolution, is linearly stable, thus representing a prototypical example of Stable Chaos.

32 pages with 19 figures, submitted to Chaos, Solitons and Fractals

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• Stochastic mean field formulation of the dynamics of diluted neural networks
• Drifting states and synchronization induced chaos in autonomous networks of excitable neurons
• Greedy optimization for growing spatially embedded oscillatory networks