Hierarchy of Chaotic Dynamics in Random Modular Networks
Łukasz Kuśmierz1, Ulises Pereira-Obilinovic1, Zhixin Lu1
1Allen Institute, Seattle, Washington, USA.
None:
We introduce a model of randomly connected neural populations and study its dynamics by means of the dynamical mean-field theory and simulations. Our analysis uncovers a rich phase diagram, featuring high- and low-dimensional chaotic phases, separated by a crossover region characterized by low values of the maximal Lyapunov exponent and participation ratio dimension, but with high values of the Lyapunov dimension that change significantly across the region. Counterintuitively, chaos can be attenuated by either adding noise to strongly modular connectivity or by introducing modularity into random connectivity. Extending the model to include a multilevel, hierarchical connectivity reveals that a loose balance between activities across levels drives the system towards the edge of chaos.
More Related Videos
Related Concept Videos
Sequence Networks of Rotating Machines
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Entropy Change in Reversible Processes
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Current Growth And Decay In RL Circuits
Stability of structures
Radical Chain-Growth Polymerization: Mechanism
Radical Chain-Growth Polymerization: Chain Branching


