Neuroscience-inspired models, such as predictive coding, have the potential
to play an important role in the future of machine intelligence. However, they
are not yet used in industrial applications due to some limitations, such as
the lack of efficiency. In this work, we address this by proposing incremental
predictive coding (iPC), a variation of the original framework derived from the
incremental expectation maximization algorithm, where every operation can be
performed in parallel without external control. We show both theoretically and
empirically that iPC is much faster than the original algorithm originally
developed by Rao and Ballard, while maintaining performance comparable to
backpropagation in image classification tasks. This work impacts several areas,
has general applications in computational neuroscience and machine learning,
and specific applications in scenarios where automatization and parallelization
are important, such as distributed computing and implementations of deep
learning models on analog and neuromorphic chips.

本研究提出了一种名为增量预测编码（iPC）的方法，通过并行执行每个操作来解决传统预测编码方法的低效性限制，实验证明 iPC 在图像分类任务上具有与反向传播相媲美的性能，适用于计算神经科学和机器学习等领域，尤其适用于分布式计算和模拟神经元芯片上的深度学习模型的实现。