提出随机图结构SNN，实现神经元可扩展、路径可复用和动态配置。
CogniSNN: Enabling Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability with Random Graph Architectures in Spiking Neural Networks
- 用随机图架构模拟大脑连接，支持神经元和路径动态生长
- 在多个数据集上性能媲美或超越现有SNN模型
- 适合需要持续学习与硬件部署的类脑智能系统
脉冲神经网络（SNN）作为第三代人工神经网络，有望弥合人工智能与计算神经科学之间的差距。然而，主流SNN研究沿用传统人工神经网络的刚性链式层级结构，忽视了大脑中神经元随机互联、形成复杂通路所具备的神经元可扩展性、路径可复用性和动态可配置性。本文提出一种新型SNN范式——认知感知脉冲神经网络（CogniSNN），通过引入随机图架构（RGA）实现上述特性。针对深层路径中的网络退化与维度不匹配问题，设计了改进的纯脉冲残差机制与自适应池化策略。进一步提出基于关键路径的学习无遗忘（KP-LwF）方法，选择性复用核心通路并保留历史知识，提升多任务迁移效率。最后，提出动态生长学习（DGL）算法，使神经元和突触可沿时间维度动态扩展。大量实验表明，CogniSNN在神经形态数据集及Tiny-ImageNet上性能达到甚至超过当前最优SNN水平。路径复用增强了跨场景持续学习能力，动态生长算法提升了抗干扰鲁棒性，并缓解了神经形态芯片部署中的固定时步限制。本工作展示了随机图结构SNN在推动类脑智能方面的潜力，为其实现硬件落地奠定了基础。
原文摘要 · Abstract (English)
Spiking neural networks (SNNs), regarded as the third generation of artificial neural networks, are expected to bridge the gap between artificial intelligence and computational neuroscience. However, most mainstream SNN research directly adopts the rigid, chain-like hierarchical architecture of traditional artificial neural networks (ANNs), ignoring key structural characteristics of the brain. Biological neurons are stochastically interconnected, forming complex neural pathways that exhibit Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability. In this paper, we introduce a new SNN paradigm, named Cognition-aware SNN (CogniSNN), by incorporating Random Graph Architecture (RGA). Furthermore, we address the issues of network degradation and dimensional mismatch in deep pathways by introducing an improved pure spiking residual mechanism alongside an adaptive pooling strategy. Then, we design a Key Pathway-based Learning without Forgetting (KP-LwF) approach, which selectively reuses critical neural pathways while retaining historical knowledge, enabling efficient multi-task transfer. Finally, we propose a Dynamic Growth Learning (DGL) algorithm that allows neurons and synapses to grow dynamically along the internal temporal dimension. Extensive experiments demonstrate that CogniSNN achieves performance comparable to, or even surpassing, current state-of-the-art SNNs on neuromorphic datasets and Tiny-ImageNet. The Pathway-Reusability enhances the network's continuous learning capability across different scenarios, while the dynamic growth algorithm improves robustness against interference and mitigates the fixed-timestep constraints during neuromorphic chip deployment. This work demonstrates the potential of SNNs with random graph structures in advancing brain-inspired intelligence and lays the foundation for their practical application on neuromorphic hardware.
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