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Researchers used computational neural network models to investigate how chronic stress affects working memory by altering the balance between excitatory and inhibitory neurons in the prefrontal cortex. They found that networks trained under conditions mimicking chronic stress (increased inhibition onto excitatory neurons) maintained performance on familiar tasks by developing sparser, more energy-efficient circuits, but showed reduced flexibility when tested on tasks requiring longer working memory duration. This work demonstrates that while neural circuits can adapt to preserve function under chronic stress, this adaptation comes at the cost of decreased generalization to novel or more demanding cognitive tasks.
Why it matters
This research provides a mechanistic explanation for why chronically stressed individuals may maintain routine cognitive functions while struggling with new or complex tasks, potentially informing therapeutic approaches for stress-related cognitive impairments. The findings suggest that interventions targeting neural circuit flexibility, rather than just stress reduction, might be necessary to restore full cognitive function.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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.
Source: Modelling chronic stress as excitation-inhibition perturbation in recurrent working-memory networks