Distinct excitatory and inhibitory bump wandering in a stochastic neural field

TL;DR

This paper models the dynamics of separate excitatory and inhibitory neural activity bumps, revealing how their interactions and noise correlations influence memory stability and variability in neural field models.

Contribution

It introduces a neural field model with distinct E/I populations and analyzes how interpopulation interactions and noise correlations affect bump dynamics and variability.

Findings

E/I bump interactions can stabilize or repel each other.

Noise correlations within and between populations shape bump position variance.

Higher interpopulation correlations surprisingly reduce variability.

Abstract

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 excitatory/inhibitory (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 excitatory/inhibitory (E/I) populations and leverage asymptotic analysis to derive a nonlinear Langevin system describing E/I bump interactions. While the E bump attracts the I bump, the I bump stabilizes but can also repel the E 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.