Why biology-inspired AI learns from almost nothing

Researchers embedded two features of real neurons—spiking dynamics and astrocyte modulation—into conventional artificial networks. The hybrid models learned new tasks from few examples and remained accurate when images were corrupted or noisy, whereas standard deep learning collapsed. This suggests biological principles offer a practical blueprint for AI systems that must work in harsh, data-starved conditions.