We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representations of objects in the same category become progressively more similar, while objects belonging to different categories become less similar. We use this observation to motivate a layer-specific learning goal in a deep network: each layer aims to learn a representational similarity matrix that interpolates between previous and later layers. We formulate this idea using a contrastive similarity matching objective function and derive from it deep neural networks with feedforward, lateral, and feedback connections and neurons that exhibit biologically plausible Hebbian and anti-Hebbian plasticity. Contrastive similarity matching can be interpreted as an energy-based learning algorithm, but with significant differences from others in how a contrastive function is constructed.

我们针对腹侧视觉通路中的观察和训练有素的深度神经网络所激发的信用分配问题提出了一种新颖的生物学上合理的解决方案。在这两种情况下，同一类别中对象的表示逐渐变得更加相似，而属于不同类别的对象则变得不那么相似。我们使用这一观察来激发深度网络中特定于层的学习目标：每一层旨在学习一个在前一层和后一层之间进行插值的表征相似性矩阵。我们使用对比相似性匹配目标函数来制定这个想法，并从中推导出具有前馈、横向和反馈连接的深度神经网络以及表现出生物学上合理的 Hebbian 和反 Hebbian 可塑性的神经元。