Recently, significant progress has been made regarding the statistical understanding of artificial neural networks (ANNs). ANNs are motivated by the functioning of the brain, but differ in several crucial aspects. In particular, it is biologically implausible that the learning of the brain is based on gradient descent. In this work we look at the brain as a statistical method for supervised learning. The main contribution is to relate the local updating rule of the connection parameters in biological neural networks (BNNs) to a zero-order optimization method.

翻译：近期，人工神经网络（ANNs）的统计学理解取得了显著进展。人工神经网络的灵感源于大脑的功能，但在多个关键方面存在差异。尤其值得注意的是，大脑的学习机制基于梯度下降在生物学上缺乏合理性。在本研究中，我们将大脑视为一种用于监督学习的统计方法。主要贡献在于将生物神经网络（BNNs）中连接参数的局部更新规则与零阶优化方法相关联。