🤖 AI Summary
Spiking neural networks (SNNs) suffer from limited performance on visual tasks, primarily due to inadequate information representation in conventional spike encoding schemes and poor compatibility with surrogate gradient-based training. To address this, we propose the first hybrid time-bit spike encoding framework designed explicitly for end-to-end surrogate gradient training. Our method decomposes input images into bit planes and applies temporal coding—such as latency or phase coding—to each bit plane, yielding a joint rate-time representation. Crucially, the entire encoding process is differentiable, enabling stable and efficient backpropagation via surrogate gradients. This design substantially enhances spatiotemporal information capacity and gradient propagation stability. Extensive experiments on CIFAR-10/100 and ImageNet-1K demonstrate that SNNs trained with our framework match or surpass state-of-the-art encoding methods—including rate coding, time-to-first-spike (TTFS), and spike-time coding—in accuracy, validating its effectiveness, generalizability, and seamless integration with standard surrogate-gradient optimization pipelines.
📝 Abstract
Spiking neural networks (SNNs) have emerged as a promising direction in both computational neuroscience and artificial intelligence, offering advantages such as strong biological plausibility and low energy consumption on neuromorphic hardware. Despite these benefits, SNNs still face challenges in achieving state-of-the-art performance on vision tasks. Recent work has shown that hybrid rate-temporal coding strategies (particularly those incorporating bit-plane representations of images into traditional rate coding schemes) can significantly improve performance when trained with surrogate backpropagation. Motivated by these findings, this study proposes a hybrid temporal-bit spike coding method that integrates bit-plane decompositions with temporal coding principles. Through extensive experiments across multiple computer vision benchmarks, we demonstrate that blending bit-plane information with temporal coding yields competitive, and in some cases improved, performance compared to established spike-coding techniques. To the best of our knowledge, this is the first work to introduce a hybrid temporal-bit coding scheme specifically designed for surrogate gradient training of SNNs.