The backpropagation algorithm, despite its widespread use in neural network learning, may not accurately emulate the human cortex's learning process. Alternative strategies, such as the Forward-Forward Algorithm (FFA), offer a closer match to the human cortex's learning characteristics. However, the original FFA paper and related works on the Forward-Forward Algorithm only mentioned very limited types of neural network mechanisms and may limit its application and effectiveness. In response to these challenges, we propose an integrated method that combines the strengths of both FFA and shallow backpropagation, yielding a biologically plausible neural network training algorithm which can also be applied to various network structures. We applied this integrated approach to the classification of the Modified National Institute of Standards and Technology (MNIST) database, where it outperformed FFA and demonstrated superior resilience to noise compared to backpropagation. We show that training neural networks with the Integrated Forward-Forward Algorithm has the potential of generating neural networks with advantageous features like robustness.

翻译：尽管反向传播算法在神经网络学习中广泛应用，但它可能无法准确模拟人类大脑皮层的学习过程。诸如前向-前向算法（FFA）等替代策略更贴近人类大脑皮层的学习特性。然而，原始FFA论文及其相关研究仅涉及非常有限的神经网络机制类型，这可能限制其应用与效果。针对这些挑战，我们提出一种集成方法，结合FFA与浅层反向传播的优势，形成一种兼具生物合理性的神经网络训练算法，并可适用于多种网络结构。我们应用该方法对改进型国家标准与技术研究院（MNIST）数据库进行分类任务，结果表明该方法在性能上优于FFA，且相比反向传播算法展现出更优的抗噪声能力。我们证明，采用集成前向-前向算法训练的神经网络具有生成鲁棒性等优势特征的潜力。