Topic: Ring attractor networks

Many animals rely on persistent internal representations of continuous angular variables for working memory, motor control, and navigation. Theories have proposed that such representations are maintained by a class of recurrently connected networks called ring attractor networks. These networks rely on large numbers of neurons to maintain continuous and stable representations and to accurately integrate incoming signals. The head direction system of the fruit fly, however, seems to achieve these properties with a remarkably small network. These findings challenge our understanding of ring attr

Efficient navigation requires animals to track their position, velocity and heading direction (HD). Some animals’ behavior suggests that they also track uncertainties about these navigational variables, and make strategic use of these uncertainties, in line with a Bayesian computation. Ring-attractor networks have been proposed to estimate and track these navigational variables, for instance in the HD system of the fruit fly Drosophila. However, such networks are not designed to incorporate a notion of uncertainty, and therefore seem unsuited to implement dynamic Bayesian inference. Here, we c

Since their introduction as a theoretical concept in the 70es by Amari, ring attractor networks have been a popular tool to explain orientation and space dependence of neural activity. Only recently, there has been an experimental proof that they are actually implemented in the fly's brain (Kim et al. 2017). While these kind of networks are useful to model systems representing only one map, like head-direction systems for example, there is a multitude of maps stored in the hippocampus (cf. panel a of the figure). This extension is taken into account by continuous attractor neural networks (CAN