Despite its widespread use in neural networks, error backpropagation has faced criticism for its lack of biological plausibility, suffering from issues such as the backward locking problem and the weight transport problem. These limitations have motivated researchers to explore more biologically plausible learning algorithms that could potentially shed light on how biological neural systems adapt and learn. Inspired by the counter-current exchange mechanisms observed in biological systems, we propose counter-current learning (CCL), a biologically plausible framework for credit assignment in neural networks. This framework employs a feedforward network to process input data and a feedback network to process targets, with each network enhancing the other through anti-parallel signal propagation. By leveraging the more informative signals from the bottom layer of the feedback network to guide the updates of the top layer of the feedforward network and vice versa, CCL enables the simultaneous transformation of source inputs to target outputs and the dynamic mutual influence of these transformations. Experimental results on MNIST, FashionMNIST, CIFAR10, and CIFAR100 datasets using multi-layer perceptrons and convolutional neural networks demonstrate that CCL achieves comparable performance to other biologically plausible algorithms while offering a more biologically realistic learning mechanism. Furthermore, we showcase the applicability of our approach to an autoencoder task, underscoring its potential for unsupervised representation learning. Our work presents a direction for biologically inspired and plausible learning algorithms, offering an alternative mechanisms of learning and adaptation in neural networks.

翻译：尽管误差反向传播在神经网络中被广泛使用，但其缺乏生物可信性，存在反向锁定问题和权重传输问题等缺陷。这些局限性促使研究人员探索更具生物可信性的学习算法，以期揭示生物神经系统适应与学习的机制。受生物系统中观察到的反向流交换机制启发，我们提出反向流学习（CCL），一种用于神经网络信用分配的生物可信框架。该框架采用前馈网络处理输入数据，反馈网络处理目标数据，两个网络通过反平行信号传播相互增强。通过利用反馈网络底层的更具信息量的信号来指导前馈网络顶层的更新，反之亦然，CCL能够同时实现源输入到目标输出的转换以及这些转换的动态相互影响。在MNIST、FashionMNIST、CIFAR10和CIFAR100数据集上使用多层感知机和卷积神经网络的实验结果表明，CCL在提供更具生物真实性的学习机制的同时，取得了与其他生物可信算法相当的性能。此外，我们展示了该方法在自编码器任务中的适用性，突显了其在无监督表示学习方面的潜力。我们的工作为受生物启发且具备生物可信性的学习算法提供了一个研究方向，为神经网络的学习与适应机制提供了替代方案。