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：最近,在对人工神经网络(ANNs)的统计理解方面取得了显著进展。 ANNs的动机是大脑的功能,但在若干关键方面有所不同。 特别是,从生物学上看,大脑的学习是以梯度下降为基础的,这是难以置信的。 在这项工作中,我们把大脑视为监督学习的统计方法。 其主要贡献是将生物神经网络(BNNs)连接参数的地方更新规则与零顺序优化方法联系起来。