相关信息最大化：一种生物合理的方法用于无权重对称性的监督深度神经网络
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
AI总结：
本文提出相关信息最大化作为生物神经网络信号传播的替代规范方法，解决了传统神经网络和反向传播算法的生物合理性问题，并提供了一种无权重对称性的解决方案。
摘要：
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，其生物合理性受到强烈批评，仍存在疑问即大脑是否采用类似反向传播的监督学习机制。本文提出层激活之间的相关信息最大化作为替代规范方法，用于描述生物神经网络中正向和反向方向的信号传播。该新框架解决了传统人工神经网络和反向传播算法的生物合理性问题。基于坐标下降优化相应目标，结合均方误差损失函数拟合标记监督数据，得到一种模拟多室锥形神经元和树突处理及侧抑制神经元的更生物合理的神经网络结构。此外，本文方法提供了解决正向和反向信号传播路径之间权重对称问题的自然解决方案，这是对传统反向传播算法合理性的重大批评。这通过利用相关互信息目标的两种替代但等价形式实现。这些替代形式本质上导致无权重对称问题的正向和反向预测网络，为这一长期挑战提供了有力的解决方案。
英文摘要：
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.
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