Modelling chronic stress as an excitatory-inhibitory perturbation in recurrent working-memory networks

Stress is an adaptive response coordinated by neural and physiological systems.

While acute stress can enhance survival, chronic stress drives structural brain changes, cognitive dysfunction, and increased psychiatric risk.

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 are still unknown.

We here investigate possible mechanisms causing inhibitory dominance using recurrent neuronal networks trained on a working memory task.

Chronic stress is modelled as a modulation in synaptic strength or neuronal activity, systematically comparing eight candidate operators against three experimentally motivated signatures of stress-induced prefrontal dysfunction: inhibitory dominance, excitatory hypofunction, and impaired task performance.

These signatures are all recovered by a single stress mechanism, stronger inhibitory-to-excitatory synapses.

Contrasting naive networks with resilient networks trained under the stress mechanism, we find that resilience training not only preserves task performance under stress, but also confines the network to the same dynamical subspace and energetic regime with and without stress.

This resilience comes at a cost: resilient networks generalise less well when the task requires longer memory than seen during training, indicating that resilient networks find a specialised solution tuned to the trained regime.

This trade-off between resilience and generalization performance persists across stress magnitude and network size, offering a computational analogue of the shift toward rigid, habit-like behaviour reported in animal following chronic stress.