The biological neural systems evolved to adapt to ecological environment for efficiency and effectiveness, wherein neurons with heterogeneous structures and rich dynamics are optimized to accomplish complex cognitive tasks. Most of the current research of biologically inspired spiking neural networks (SNNs) are, however, grounded on a homogeneous neural coding scheme, which limits their overall performance in terms of accuracy, latency, efficiency, and robustness, etc. In this work, we argue that one should holistically design the network architecture to incorporate diverse neuronal functions and neural coding schemes for best performance. As an early attempt in this research direction, we put forward a hybrid neural coding framework that integrates multiple neural coding schemes discovered in neuroscience. We demonstrate that the proposed hybrid coding scheme achieves a comparable accuracy with the state-of-the-art SNNs with homogeneous neural coding on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets with less than eight time steps and at least 3.90x fewer computations. Furthermore, we demonstrate accurate, rapid, and robust sound source localization on SoClas dataset. This study yields valuable insights into the performance of various hybrid neural coding designs and hold significant implications for designing high performance SNNs.

翻译：生物神经系统在演化过程中为适应生态环境而实现了高效能，其中具有异质结构和丰富动力学的神经元经过优化以完成复杂认知任务。然而，当前大多数生物启发的脉冲神经网络（SNNs）研究均基于同质神经编码方案，这限制了其在精度、延迟、效率及鲁棒性等方面的整体性能。本研究提出，为实现最优性能，应当从整体上设计网络架构以整合多样的神经元功能与神经编码方案。作为该研究方向的首批尝试，我们提出了一种整合神经科学中多种神经编码机制的混合神经编码框架。实验表明，所提出的混合编码方案在CIFAR-10、CIFAR-100和Tiny-ImageNet数据集上，以少于8个时间步且计算量至少减少3.90倍的条件下，达到了与采用同质编码的最先进SNNs相当的精度。此外，我们在SoClas数据集上实现了声源定位的精准、快速与鲁棒性。本研究揭示了不同混合神经编码设计的性能差异，对设计高性能SNNs具有重要指导意义。