Deep neural networks (DNNs), while achieving significant performance in areas like image recognition, frequently struggle with generalization, learning from sparse examples, and continuous adaptation—abilities that are fundamental to biological neural systems. This persistent limitation stems from DNNs' inability to effectively emulate the adaptive and efficient learning mechanisms intrinsic to biological networks. Research is now focused on guiding sparse neural networks using neurobiological principles to cultivate biologically plausible representations1. This approach aims to instill artificial intelligence with a more robust and adaptable learning paradigm by mimicking the structural and functional efficiencies observed in natural intelligence. The objective is to enhance AI's capacity for robust generalization across novel data, accelerate skill acquisition from minimal training data, and facilitate continuous learning without catastrophic forgetting. For practitioners and AI developers, advances in these core AI capabilities could translate into more resilient anomaly detection systems, adaptive threat intelligence, and robust automated defense mechanisms capable of learning from and responding to emergent threats.