用类脑机制实现无需反向传播的鲁棒图像分类
RECAP: Local Hebbian Prototype Learning as a Self-Organizing Readout for Reservoir Dynamics
- 通过突触可塑性原理自组织构建原型，匹配输入特征
- 在MNIST-C数据集上对各类干扰保持稳定识别性能
- 适合追求生物合理性与在线学习的神经网络研究者
大脑的鲁棒感知常归因于高维群体活动与局部可塑性机制协同强化重复结构。而现代图像识别系统多依赖误差反向传播与端到端梯度优化，难以契合局部计算与局部可塑性。我们提出RECAP（基于赫布共激活原型的回响计算），一种生物启发的学习策略，将未训练的回响动态与自组织赫布原型读出相结合。RECAP将时间平均的回响响应离散化为激活水平，构建单元对间的共激活掩码，并通过类赫布的增强-衰减规则增量更新类别专属原型矩阵。推理通过原型重叠匹配完成。该方法避免了误差反向传播，天然支持在线原型更新。我们在MNIST-C数据集上展示了其鲁棒性：即使未经受污染样本训练，仍能抵抗多种类型干扰。
原文摘要 · Abstract (English)
Robust perception in brains is often attributed to high-dimensional population activity together with local plasticity mechanisms that reinforce recurring structure. In contrast, most modern image recognition systems are trained by error backpropagation and end-to-end gradient optimization, which are not naturally aligned with local computation and local plasticity. We introduce RECAP (Reservoir Computing with Hebbian Co-Activation Prototypes), a bio-inspired learning strategy for robust image classification that couples untrained reservoir dynamics with a self-organizing Hebbian prototype readout. RECAP discretizes time-averaged reservoir responses into activation levels, constructs a co-activation mask over reservoir unit pairs, and incrementally updates class-wise prototype matrices via a Hebbian-like potentiation-decay rule. Inference is performed by overlap-based prototype matching. The method avoids error backpropagation and is naturally compatible with online prototype updates. We illustrate the resulting robustness behavior on MNIST-C, where RECAP remains robust under diverse corruptions without exposure to corrupted training samples.
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