Interareal coupling reduces encoding variability in multi-area models of spatial working memory

Persistent activity observed during delayed-response tasks for spatial working memory (Funahashi et al., 1989) has commonly been modeled by recurrent networks whose dynamics is described as a bump attractor (Compte et al., 2000). We examine the effects of interareal architecture on the dynamics of bump attractors in stochastic neural fields. Lateral inhibitory synaptic structure in each area sustains stationary bumps in the absence of noise. Introducing noise causes bumps in individual areas to wander as a Brownian walk. However, coupling multiple areas together can help reduce the variability of the bump's position in each area. To examine this quantitatively, we approximate the position of the bump in each area using a small noise expansion that also assumes weak amplitude interareal projections. Our asymptotic results show the motion of the bumps in each area can be approximated as a multivariate Ornstein-Uhlenbeck process. This shows reciprocal coupling between areas can always reduce variability, if sufficiently strong, even if one area contains much more noise than the other. However, when noise is correlated between areas, the variability-reducing effect of interareal coupling is diminished. Our results suggest that distributing spatial working memory representations across multiple, reciprocally-coupled brain areas can lead to noise cancelation that ultimately improves encoding.