Skip to Main Content
This work aims at studying dynamical models of neural networks, which exhibit transitions between quasistable states of various complexities. We use the biologically motivated KIII model, which is a high-dimensional dynamical system with extremely fragmented boundaries between limit cycles, tori, fixed points, and chaotic attractors. We study the role of additive noise in the development of itinerant trajectories. Noise broadens the region of the dominance of chaotic attractors. This result is especially useful in the application of KIII and makes it possible to select parameter regions where KIII can operate as a robust dynamic system and associative memory device.