We analyze the properties of networks obtained from the trajectories of unimodal maps at the transi- tion to chaos via the horizontal visibility (HV) algorithm. We find that the network degrees fluctuate at all scales with amplitude that increases as the size of the network grows, and can be described by a spectrum of graph-theoretical generalized Lyapunov exponents. We further define an entropy growth rate that describes the amount of information created along paths in network space, and find that such en- tropy growth rate coincides with the spectrum of generalized graph-theoretical exponents, constituting a set of Pesin-like identities for the network
International audienceRandom neural networks are dynamical descriptions of randomly interconnected n...
In many applications, there is a desire to determine if the dynamics of interest are chaotic or not....
International audienceRandom neural networks are dynamical descriptions of randomly interconnected n...
We examine the connectivity fluctuations across networks obtained when the horizontal visibility (HV...
The recently formulated theory of horizontal visibility graphs transforms time series into graphs an...
Time series are proficiently converted into graphs via the horizontal visibility (HV) algorithm, whi...
The type-I intermittency route to (or out of) chaos is investigated within the horizontal visibility...
<p>We plot the numerical values of and for (the numerical step is and in each case the processed...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
"We investigate how changes of specific topological features result on transitions among different b...
International audienceRandom neural networks are dynamical descriptions of randomly interconnected n...
International audienceRandom neural networks are dynamical descriptions of randomly interconnected n...
In many applications, there is a desire to determine if the dynamics of interest are chaotic or not....
International audienceRandom neural networks are dynamical descriptions of randomly interconnected n...
We examine the connectivity fluctuations across networks obtained when the horizontal visibility (HV...
The recently formulated theory of horizontal visibility graphs transforms time series into graphs an...
Time series are proficiently converted into graphs via the horizontal visibility (HV) algorithm, whi...
The type-I intermittency route to (or out of) chaos is investigated within the horizontal visibility...
<p>We plot the numerical values of and for (the numerical step is and in each case the processed...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of ...
"We investigate how changes of specific topological features result on transitions among different b...
International audienceRandom neural networks are dynamical descriptions of randomly interconnected n...
International audienceRandom neural networks are dynamical descriptions of randomly interconnected n...
In many applications, there is a desire to determine if the dynamics of interest are chaotic or not....
International audienceRandom neural networks are dynamical descriptions of randomly interconnected n...