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While advances in artificial intelligence and neuroscience have enabled theemergence of neural networks capable of learning a wide variety of tasks, ourunderstanding of the temporal dynamics of these networks remains limited. Here,we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF)neural networks in tasks of continual familiarity detection. Drawinginspiration from the field of network neuroscience, we examine the network'sdynamic reconfiguration, focusing on how network modules evolve throughoutlearning. Through a comprehensive assessment involving metrics like networkaccuracy, modular flexibility, and distribution entropy across diverse learningmodes, our approach reveals various previously unknown patterns of networkreconfiguration. In particular, we find that the emergence of networkmodularity is a salient predictor of performance, and that modularizationstrengthens with increasing flexibility throughout learning. These insights notonly elucidate the nuanced interplay of network modularity, accuracy, andlearning dynamics but also bridge our understanding of learning in artificialand biological realms.

Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection