This paper introduces Bio-Inspired Mamba (BIM), a novel online learning framework for selective state space models that integrates biological learning principles with the Mamba architecture. BIM combines Real-Time Recurrent Learning (RTRL) with Spike-Timing-Dependent Plasticity (STDP)-like local learning rules, addressing the challenges of temporal locality and biological plausibility in training spiking neural networks. Our approach leverages the inherent connection between backpropagation through time and STDP, offering a computationally efficient alternative that maintains the ability to capture long-range dependencies. We evaluate BIM on language modeling, speech recognition, and biomedical signal analysis tasks, demonstrating competitive performance against traditional methods while adhering to biological learning principles. Results show improved energy efficiency and potential for neuromorphic hardware implementation. BIM not only advances the field of biologically plausible machine learning but also provides insights into the mechanisms of temporal information processing in biological neural networks.

翻译：本文提出生物启发的Mamba（BIM），一种新颖的用于选择性状态空间模型的在线学习框架，该框架将生物学习原理与Mamba架构相结合。BIM将实时循环学习（RTRL）与类脉冲时间依赖可塑性（STDP）的局部学习规则相结合，解决了训练脉冲神经网络中时间局部性和生物合理性的挑战。我们的方法利用了通过时间的反向传播与STDP之间的内在联系，提供了一种计算高效的替代方案，同时保持了捕获长程依赖关系的能力。我们在语言建模、语音识别和生物医学信号分析任务上评估了BIM，结果表明其在遵循生物学习原理的同时，与传统方法相比具有有竞争力的性能。结果显示其能效得到改善，并具备在神经形态硬件上实现的潜力。BIM不仅推动了生物合理性机器学习领域的发展，也为生物神经网络中时间信息处理的机制提供了见解。