Distinct Excitatory and Inhibitory Bump Wandering in a Stochastic Neural Field

Localized persistent cortical neural activity is a validated neural substrate of parametric working memory. Such activity "bumps" represent the continuous location of a cue over several seconds. Pyramidal (excitatory ($$)) and interneuronal (inhibitory ($$)) subpopulations exhibit tuned bumps of activity, linking neural dynamics to behavioral inaccuracies observed in memory recall. However, many bump attractor models collapse these subpopulations into a single joint $$/$$(lateral inhibitory) population and do not consider the role of interpopulation neural architecture and noise correlations. Both factors have a high potential to impinge upon the stochastic dynamics of these bumps, ultimately shaping behavioral response variance. In our study, we consider a neural field model with separate $$/$$ populations and leverage asymptotic analysis to derive a nonlinear Langevin system describing $$/$$ bump interactions. While the $$ bump attracts the $$ bump, the $$ bump stabilizes but can also repel the $$ bump, which can result in prolonged relaxation dynamics when both bumps are perturbed. Furthermore, the structure of noise correlations within and between subpopulations strongly shapes the variance in bump position. Surprisingly, higher interpopulation correlations reduce variance.