Title: Difference Predictive Coding for Training Spiking Neural Networks
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Spiking neural networks, predictive coding, biologically plausible learning, neuromorphic computing, difference predictive coding, local learning rules, energy efficiency, communication efficiency, spike-based learning, surrogate gradient alternatives

Abstract: Predictive coding networks (PCNs) offer a local-learning alternative to backpropagation in which layers communicate residual errors, aligning well with biological computation and neuromorphic hardware. In this work we introduce \emph{Difference Predictive Coding} (DiffPC), a spike-native PC formulation for spiking neural networks. DiffPC replaces dense floating-point messages with sparse ternary spikes, provides spike-compatible target and error updates, and employs adaptive threshold schedules for event-driven operation. On MNIST and Fashion-MNIST, DiffPC attains 99.3\% and 89.6\% test accuracy, respectively, while maintaining low spike counts during training. As such, DiffPC supports energy-efficient training and inference, offering a hardware-aligned alternative to backpropagation for neuromorphic platforms.

TL;DR: We create a spiking neural network compatible learning algorithm based on predictive coding theory