Oscillatory dynamics in an attractor neural network with firing rate adaptation

We develop a framework for generating oscillations in ring attractor networks with firing rate adaptation. We show the relationship between the frequency of rotation around the ring of the shifting bump of activity, the adaptation variable and other model parameters using perturbation theory. The analytic solutions are validated against simulations of such networks. Further preliminary findings indicate that the frequency of these networks can be simply controlled using an external stimulus. The mechanism developed here could potentially be used for temporal coding of position through interference of oscillators of different frequencies.