Spiking neural networks (SNNs) are designed for low-power neuromorphic computing. A widely adopted hybrid paradigm decouples feature extraction from classification to improve biological plausibility and modularity. However, this decoupling concentrates decision making in the downstream classifier, which in many systems becomes the limiting factor for both accuracy and efficiency. Hand-preset, fixed topologies risk either redundancy or insufficient capacity, and surrogate-gradient training remains computationally costly. Biological neurogenesis is the brain’s mechanism for adaptively adding new neurons to build efficient, task-specific circuits. Inspired by this process, we propose the neurogenesis-inspired spiking neural network (NG-SNN), a dynamic adaptive framework that uses two key innovations to address these challenges. Specifically, we first introduce a supervised incremental construction mechanism that dynamically grows a task-optimal structure by selectively integrating neurons under a contribution criterion. Second, we devise an activity-dependent analytical learning method that replaces iterative optimization with single-shot and adaptive weight computation for each structural update, drastically improving training efficiency. Therefore, NG-SNN uniquely integrates dynamic structural adaptation with efficient non-iterative learning, forming a self-organizing and rapidly converging classification system. Moreover, this neurogenesis-driven process endows NG-SNN with a highly compact structure that requires significantly fewer parameters. Extensive experiments demonstrate that our NG-SNN matches or outperforms its competitors on diverse datasets, without the overhead of iterative training and manual architecture tuning.
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