TY  - JOUR
PY  - 2022//
TI  - Noise-activated barrier crossing in multiattractor dissipative neural networks
JO  - Physical review. E
A1  - Taylor, Joseph D.
A1  - Chauhan, Ashok S.
A1  - Taylor, John T.
A1  - Shilnikov, Andrey L.
A1  - Nogaret, Alain
SP  - 064203
EP  - 064203
VL  - 105
IS  - 6-1
N2  - Noise-activated transitions between coexisting attractors are investigated in a chaotic spiking network. At low noise level, attractor hopping consists of discrete bifurcation events that conserve the memory of initial conditions. When the escape probability becomes comparable to the intrabasin hopping probability, the lifetime of attractors is given by a detailed balance where the less coherent attractors act as a sink for the more coherent ones. In this regime, the escape probability follows an activation law allowing us to assign pseudoactivation energies to limit cycle attractors. These pseudoenergies introduce a useful metric for evaluating the resilience of biological rhythms to perturbations.<p />  <p>Language: en</p>
LA  - en
SN  - 2470-0045
UR  - http://dx.doi.org/10.1103/PhysRevE.105.064203
ID  - ref1
ER  -