@article{10902/35348,
year = {2024},
month = {7},
url = {https://hdl.handle.net/10902/35348},
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 ɸ(x) = tanh x. A discontinuous transition to chaos occurs whenever the slope of ɸ at 0 is a local minimum [i.e., for ɸ(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.},
organization = {I acknowledge support by Project No. PID2021-125543NBI00, funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe.},
publisher = {American Physical Society},
publisher = {Physical Review E, 2024, 110(1), 014201},
title = {Discontinuous transition to chaos in a canonical random neural network},
author = {Pazó Bueno, Diego Santiago},
}