Modelling chronic stress as excitation-inhibition perturbation in recurrent working-memory networks

Abstract

Chronic stress can turn an organism's stress response maladaptive, with consequences including behavioral inflexibility and impaired working memory. At the cellular level, chronic stress shifts the excitatory-inhibitory (E/I) balance of prefrontal pyramidal neurons toward inhibitory dominance, yet the mechanisms underlying these alterations, as well as their effect on network performance, are still unknown. We use recurrent E/I networks to isolate a chronic-stress-associated increase in inhibition and examine its consequences for inhibitory dominance, excitatory hypofunction, and computation. We then ask how stress adaptation preserves function and what computational costs adaptation entails. We model the effects of stress as selective stochastic strengthening of inhibitory synapses onto excitatory neurons. Applying this stress perturbation to neural networks trained on a working-memory task, task performance drops, as would be expected under chronic stress. In contrast, networks trained under strengthened inhibition preserve task performance and develop inhibitory dominance. These resilient networks stabilise recurrent dynamics under stress and an energetic proxy, and produce a sparser, less reciprocally connected circuit. These stress adaptations, however, decrease performance of resilient networks compared to networks trained without stress for tasks requiring longer working-memory. This resilience-generalisation trade-off persists across network size and stress magnitude used during training. Comparing eight candidate perturbations on synaptic strength or neuronal excitability confirms that strengthened inhibition best reproduces the signatures of stress among those tested. Our results suggest that adaptation on the network level can preserve familiar computations under stress, yet stress adaptation specialises circuits such that flexibility outside the trained regime is limited.