Topic: angular velocity

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

Head direction (HD) cells track an animal’s head direction in darkness by integrating angular velocity signals, a phenomenon called path integration (PI). Ring attractor models for angular PI have received strong experimental support. To function as integrators, HD circuits require precisely tuned connectivity, which is costly to pass down genetically. This suggests that synaptic plasticity is crucial in setting up these circuits. We propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic c