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；然而，其生物学可解释性一直备受质疑，大脑是否采用类似的有监督学习机制仍是一个开放性问题。本文提出将层激活之间的关联信息最大化作为一种替代性规范方法，用以描述生物神经网络中正向和反向的信号传播。这一新框架解决了传统人工神经网络与反向传播算法在生物学可解释性方面的诸多问题。基于坐标下降法优化相应目标函数，并结合用于拟合标注监督数据的均方误差损失函数，由此产生的神经网络结构模拟了一个更具生物真实性的多区室锥体神经元网络，该网络具备树突处理功能及侧向抑制神经元。此外，我们的方法自然地解决了正向与反向信号传播路径之间的权重对称问题——这一直是对传统反向传播算法可解释性的重大质疑。这是通过利用关联互信息目标的两种替代但等价的形式实现的。这些替代形式本质上构建了无权重对称问题的正向与反向预测网络，为这一长期存在的挑战提供了令人信服的解决方案。