The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks, however, its biological-plausibility is disputed, 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，然而其生物合理性一直存在争议，大脑是否采用类似的有监督学习机制仍是一个未解之谜。本文提出将层激活之间的关联信息最大化作为另一种规范化方法，用于描述生物神经网络中正向和反向信号传播过程。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疑虑。基于坐标下降法优化相应目标函数，并结合用于拟合有监督标签数据的均方误差损失函数，我们构建了一种神经网络结构，该结构模拟了更具生物真实性的多隔室锥体神经元网络（包含树突处理和侧向抑制神经元）。此外，我们的方法自然解决了正向与反向信号传播路径之间的权重对称问题——这恰是对传统反向传播算法合理性的主要质疑。通过利用关联互信息目标的两种替代但等价形式，我们实现了这一突破：这些替代形式内在隐含着正向与反向预测网络，且不存在权重对称问题，为这一长期存在的挑战提供了极具说服力的解决方案。