The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks; however, its biological plausibility has been strongly criticized, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximization between layer activations as an alternative normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This new framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. The coordinate descent-based optimization of the corresponding objective, combined with the mean square error loss function for fitting labeled supervision data, gives rise to a neural network structure that emulates a more biologically realistic network of multi-compartment pyramidal neurons with dendritic processing and lateral inhibitory neurons. Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm. This is achieved by leveraging two alternative, yet equivalent forms of the correlative mutual information objective. These alternatives intrinsically lead to forward and backward prediction networks without weight symmetry issues, providing a compelling solution to this long-standing challenge.

翻译：反向传播算法在训练大规模人工神经网络方面取得了显著成功，但其生物合理性一直备受质疑，大脑是否采用类似的有监督学习机制仍是一个未解之谜。本文提出将层间激活的相关性信息最大化作为替代性规范方法，用以描述生物神经网络中前向与后向的信号传播过程。这一新框架解决了传统人工神经网络及反向传播算法在生物合理性方面的诸多争议。通过基于坐标下降法优化相应目标函数，并结合拟合有标签监督数据所需的均方误差损失函数，我们构建出一种更贴近生物现实的神经网络结构，该结构模拟了具有树突处理机制的多室锥体神经元与侧向抑制神经元的协同作用。此外，我们的方法为前向与后向信号传播路径间的权重对称性问题提供了自然解决方案——这是传统反向传播算法合理性面临的关键质疑。通过利用相关性互信息目标函数的两种等价替代形式，我们从根本上构建了无权重对称性问题的前向与后向预测网络，为这一长期难题提供了令人信服的解决方案。