A simple computational model of spontaneous neural dynamics controlling an agent in a virtual environment shows that brain-environment feedback can rapidly destabilize neural and behavioral dynamics, requiring homeostatic mechanisms. Local homeostatic plasticity, where inhibition adjusts to balance excitation, and global mechanisms, where regional task-negative activity compensates for task-positive sensory input in another region, both stabilize behavior. The results suggest complementary functional roles for local and macroscale homeostatic processes and propose a novel function for macroscopic task-negative activity patterns, such as the default mode network, in maintaining stable neural and behavioral dynamics.
Emergence describes how complex systems exhibit properties not easily explained by their individual parts, seen in ant colonies, bird flocks, or brain function. This paper clarifies the concept, distinguishing strong emergence (where properties are irreducible to lower-level mechanisms) from weak emergence (where properties arise from but are explainable by lower-level interactions). It argues that models based on strong emergence, such as the free energy principle and integrated information theory, risk metaphysical implausibility and overdetermination, making them only one of many possible explanations. In contrast, weakly emergent computational models like oscillatory networks, which start from biologically plausible elementary units, are ontologically sound and offer a powerful approach for future neuroscientific research.