Key parameters.

Experimental, and computational studies have highlighted the abundance and significance of excitability and synaptic heterogeneity for network resilience, learning, and memory. However, these studies have been confined to cellular-level investigations, a spatial resolution that is inaccessible with clinical tools (i.e., electroencephalography, magnetoencephalography). Such clinical recordings capture local field potentials, representing brain activity at a coarser spatial scale than individual neurons. To understand how neuronal diversity affects large-scale activity, computational models and techniques are needed to examine the effects of heterogeneity on dynamics at these coarser scales. We therefore examine how intrinsic excitability heterogeneity in neuronal populations of the brain affects the stability and resilience of macro-scale brain networks against external stimulations. We use a macro-scale computational model where each node is a neural mass model with interacting excitatory and inhibitory sub-populations. Heterogeneities are represented using lumped parameters, and brain region dynamics are coupled through a global synaptic coupling parameter. Our numerical results show that excitability heterogeneity and synaptic coupling stabilize neural dynamics against external inputs, reducing amplitude variations and enhancing resilience at the macro-scale. Excitability heterogeneity also prevents the emergence of multiple equilibria. Although the global coupling parameter alone is less effective at reducing the emergence of multiple equilibria, it boosts the network’s resilience when combined with heterogeneity. Thus, excitability heterogeneity stabilizes neural dynamics and simplifies the system’s stable states on a broader spatial scale.