  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.
