<< Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general--they are destroyed by most infinitesimal changes of the dynamical law that defines them. >>️
AA << build on the persistent manifold theory to explain the commonalities between bifurcations from and approximations of continuous attractors. Fast-slow decomposition analysis uncovers the persistent manifold that survives the seemingly destructive bifurcation. Moreover, recurrent neural networks trained on analog memory tasks display approximate continuous attractors with predicted slow manifold structures. >>️
<< continuous attractors are functionally robust and remain useful as a universal analogy for understanding analog memory. >>
Ábel Ságodi, Guillermo Martín-Sánchez, Piotr Sokół, Il Memming Park. Back to the Continuous Attractor. arXiv: 2408.00109v1 [q-bio.NC]. Jul 31, 2024.
Also: attractor, analogy, brain, in https://www.inkgmr.net/kwrds.html
Keywords: gst, attractor, continuous attractor, analogy, brain
