Developing biologically plausible learning algorithms that can achieve performance comparable to error backpropagation remains a longstanding challenge. Existing approaches often compromise biological plausibility by entirely avoiding the use of spikes for error propagation or relying on both positive and negative learning signals, while the question of how spikes can represent negative values remains unresolved. To address these limitations, we introduce Bidirectional Spike-based Distillation (BSD), a novel learning algorithm that jointly trains a feedforward and a backward spiking network. We formulate learning as a transformation between two spiking representations (i.e., stimulus encoding and concept encoding) so that the feedforward network implements perception and decision-making by mapping stimuli to actions, while the backward network supports memory recall by reconstructing stimuli from concept representations. Extensive experiments on diverse benchmarks, including image recognition, image generation, and sequential regression, show that BSD achieves performance comparable to networks trained with classical error backpropagation. These findings represent a significant step toward biologically grounded, spike-driven learning in neural networks. Our code is available at https://github.com/alden199/Bidirectional-Spike-Based-Distillation.

翻译：开发性能可与误差反向传播相媲美的生物可信学习算法，长期以来一直是一个挑战。现有方法通常通过完全避免使用脉冲进行误差传播，或依赖正负两种学习信号来折衷生物可信性，而脉冲如何表示负值的问题仍未解决。为应对这些局限，我们提出了双向脉冲蒸馏（BSD），这是一种联合训练前向和反向脉冲网络的新型学习算法。我们将学习表述为两种脉冲表示（即刺激编码和概念编码）之间的转换，使得前向网络通过将刺激映射到动作来实现感知与决策，而反向网络则通过从概念表示重构刺激来支持记忆回忆。在包括图像识别、图像生成和序列回归在内的多种基准测试上的广泛实验表明，BSD 达到了与经典误差反向传播训练网络相当的性能。这些发现代表了在实现基于生物机制、脉冲驱动的神经网络学习方面迈出的重要一步。我们的代码可在 https://github.com/alden199/Bidirectional-Spike-Based-Distillation 获取。