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, the locality in the updating rule of the connection parameters in biological neural networks (BNNs) makes it 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 BNNs to a zero-order optimization method. It is shown that the expected values of the iterates implement a modification of gradient descent.

翻译：近期，人工神经网络的统计学理解取得了显著进展。人工神经网络受大脑运作机制的启发，但在多个关键方面存在差异。特别地，生物神经网络中连接参数的局部更新规则使得大脑学习基于梯度下降的假设在生物学上缺乏合理性。本研究将大脑视为一种监督学习的统计方法，主要贡献在于将生物神经网络的局部更新规则与零阶优化方法相关联。研究表明，其迭代过程的期望值实现了一种梯度下降的修正形式。