Oscillatory network spontaneously recovers both activity and robustness after prolonged removal of neuromodulators

Resilience of circuits to environmental challenge

Animals of all kinds evolved to deal with anticipated and unanticipated changes in a variety of features in their environments. Consequently, all environmental perturbations, adaptations, and acclimation involve a myriad of factors that, together, contribute to environmental resilience. New work highlights the importance of neuromodulation in the control of environmental resilience, and illustrates that different components of the nervous system may be differentially resilient to environmental perturbations. Climate change is today pushing animals to deal with previously unanticipated environmental challenges, and therefore understanding the complex biology of adaptation and acclimation to various environmental conditions takes on new urgency.

Diversity-induced trivialization and resilience of neural dynamics

Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.