Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems.

翻译：近日，受生物启发的脉冲神经网络（SNNs）在解决模式识别任务中展现出良好潜力。然而，现有SNN基于同质神经元，采用统一神经编码进行信息表征。鉴于每种神经编码方案各有优劣，此类SNN在实现精度、响应时间、效率及鲁棒性等实际应用关键性能指标时面临挑战。本研究认为SNN架构应整体设计以融合异质编码方案。作为该方向的初步探索，我们提出混合神经编码与学习框架：该框架包含神经科学发现的多样化编码方案构成的"神经编码库"，结合灵活的任务特定编码分配策略，以及新型逐层学习方法以有效实现混合编码SNN。在图像分类与声源定位任务中，我们验证了所提框架的优越性：具体而言，混合编码SNN在达到最先进SNN同等精度的同时，推理延迟与能耗显著降低，并展现出高噪声鲁棒性。本研究为混合神经编码设计提供重要见解，为开发高性能神经形态系统奠定基础。