Eric Shea-Brown
Courant Institute for Mathematical Sciences, New York University
 

From Spikes to Speed-Accuracy via the Locus Coeruleus

This talk covers models of neural dynamics at two different scales: abstracted neural integrators, neural subpopulations involved in simple decision tasks, and smaller-scale models of neural oscillators in the brainstem area locus coeruleus (LC), which may control and optimize these integrator networks. Via a phase reduction and probability density formulation, neural oscillators are used to model and indicate mechanisms for distinct firing patterns in the LC that have been linked with distinct levels of performance in simple cognitive tasks. A possible role for these firing patterns in optimizing speed and accuracy in decision tasks is then studied via stochastic firing rate equations for neural integrators. In particular, we study a possible role for the LC in dynamically optimizing the gain of these integrators. This joint work with Jeff Moehlis, Phil Holmes, Mark Gilzenrat, and Jonathan Cohen.