Related Experiment Video
Updated: Jun 16, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Discontinuous transition to chaos in a canonical random neural network
1<a href="https://ror.org/040kx1j83">Instituto de Física de Cantabria (IFCA)</a>, Universidad de Cantabria-CSIC, 39005 Santander, Spain.
Abstract:
We study a paradigmatic random recurrent neural network introduced by Sompolinsky, Crisanti, and Sommers (SCS). In the infinite size limit, this system exhibits a direct transition from a homogeneous rest state to chaotic behavior, with the Lyapunov exponent gradually increasing from zero. We generalize the SCS model considering odd saturating nonlinear transfer functions, beyond the usual choice ϕ(x)=tanhx. A discontinuous transition to chaos occurs whenever the slope of ϕ at 0 is a local minimum [i.e., for ϕ^{'''}(0)>0]. Chaos appears out of the blue, by an attractor-repeller fold. Accordingly, the Lyapunov exponent stays away from zero at the birth of chaos.
Related Concept Videos
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.
Random Variables
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Propagation of Uncertainty from Random Error
Random Error
Classification of Systems-II
BIBO stability of continuous and discrete -time systems
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....

