Stable-yet-switchable (SyS) attractor networks

Recurrent neural networks functioning as associative memories are often studied and optimized for recall quality and capacity, with the focus primarily on the network´s stability, i.e., convergence to stored attractors. However, the ability of networks to switch between attractors in a controlled way is also potentially a useful phenomenon. Networks that are stable under most conditions, but can be switched by specific stimuli may be used to model cognitive control and other timevarying cognitive phenomena. Such networks, which we term stable-yet-switchable (SyS) networks, are also of interest from the networks perspective, and the SyS properties of scale-free networks have been noted by researchers. In this paper, we consider networks with bimodal connectivity - a core of densely connected neurons and a larger periphery with sparser connectivity - and compare their SyS performance with random and scale-free recurrent neural networks. The results show that core-periphery networks have much better SyS performance than scale-free networks.