提出模块化脉冲神经网络，提升能效与抗遗忘能力
The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing
- 将分类路径拆分为独立专家，消除全局连接纠缠
- 参数量少10倍，准确率媲美全连接网络，支持低频发放
- 适合边缘计算场景，决策过程可审计、抗灾难性遗忘
生物神经系统的高效与鲁棒源于分隔化架构。相比之下，现代人工神经网络依赖全局耦合结构，导致决策逻辑不透明且易产生灾难性遗忘。本文提出一种可分解脉冲神经网络（D-SNN），通过结构隔离分类路径，消除全局突触纠缠。基于类生物学的推-拉损失函数优化，D-SNN在MNIST、Fashion-MNIST以及CIFAR-10/100基准上达到有竞争力的准确率。该模块化方法在性能相当的情况下，仅需全连接网络十分之一的参数量。此外，网络运行时的发放频率和突触操作次数降低数个数量级。物理切断专家间连接后，序列学习中具备天然抗遗忘能力。关键的是，这些独立路径生成可审计的神经信号，显著提升决策透明度。这一仿生可验证架构为资源受限的边缘环境部署确定性类脑智能提供了高效基础。
原文摘要 · Abstract (English)
Biological neural systems achieve high efficiency and robustness through compartmentalized architectures. In contrast, modern artificial neural networks rely on globally entangled structures, which obscure decision logic and suffer from catastrophic forgetting. Here, we report a Decomposable Spiking Neural Network (D-SNN) that eliminates global synaptic entanglement by structurally isolating classification pathways into independent experts. Optimized via a bio-inspired push-pull loss function, the D-SNN achieves competitive accuracies on MNIST, Fashion-MNIST, and CIFAR-10/100 benchmarks. This modular approach matches the performance of fully dense networks while utilizing an order of magnitude fewer parameters. In addition, our networks operate with up to several orders of magnitude lower firing rates and fewer synaptic operations. Furthermore, physically severing connections between experts provides inherent protection against catastrophic forgetting during sequential learning. Crucially, these isolated pathways generate auditable neural signals, increasing decision transparency. This biomimetic, verifiable architecture establishes an efficient foundation for deploying deterministic neuromorphic intelligence in resource-constrained edge environments.
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