2026-10-15

Stress reshapes cortico-limbic dynamics across multiple timescales, with important consequences for healthy and pathological arousal responses. We develop and analyze a Wilson-Cowan model of prefrontal-amygdala circuitry, incorporating excitatory and inhibitory populations involved in emotional regulation. The effects of stress cortisol are represented as history-dependent modulations of two stress-sensitive interactions: recurrent prefrontal excitation and inhibition of basolateral amygdala principal cells. Using numerical simulations and bifurcation analysis, we examine how this self-interacting system organizes transitions between stable activity and infra-slow oscillations, and how this structure changes with cortisol-related temporal memory. To distinguish the effects of memory temporal distribution from those of its timescale, we compare weak and strong Gamma kernels with a discrete-delay limit, representing progressively different ways in which past activity can influence current coupling. Introducing temporal memory reshapes the parameter region supporting oscillations and alters their persistence. Weak and strong Gamma kernels produce remarkably similar global dynamics, whereas the discrete-delay limit generates a substantially richer bifurcation structure, including quasiperiodic activity. Together, these results suggest that the dynamical effects of cortisol depend not only on the characteristic timescale of its action, but also on the shape of the temporal memory through which past neural activity influences the present. Different forms of temporal integration may provide a mechanism by which neural systems implement distinct functional dynamics, while changes in this integration could promote atypical or pathological rhythmic regimes.

Abundant experimental data support the view that motion detection is a canonical neural operation, common to a wide variety of species and different stages in the visual stream. However, existing motion models lack this generality, as their predictive power tends to be restricted to some specific visual phenomena, brain regions, or animal taxa. Here, we introduce a neural circuit model that only uses cells selective to cardinal directions, with elements that can be interpreted as emulating some dendritic processes, and we show how it is able to predict a wide range of visual motion data, from basic properties to hitherto unexplained phenomena, surpassing state-of-the-art models implemented as deep neural networks with millions of parameters. The direction-selective cells that our model relies on are present in the visual systems of many classes of animals, from insects to primates, and in visual pathway structures going from the retina to the cortex, suggesting that the proposed model could serve as a canonical neural circuit for estimating motion direction.