Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today’s dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning and predictive coding networks. We argue that the shallow brain architecture provides several critical benefits over deep hierarchical structures and a more complete depiction of how mammalian brains achieve fast and flexible computational capabilities.

深度学习和预测编码架构通常假设神经网络中的推理是分层的。然而，深度学习和预测编码架构中很大程度上被忽视的是神经生物学证据，即所有分层皮层区域（无论较高还是较低）都直接投射到皮层下区域并直接从皮层下区域接收信号。考虑到这些神经解剖学事实，当今以皮质为中心的分层架构在深度学习和预测编码网络中的主导地位是非常值得怀疑的。这种架构可能缺少大脑使用的基本计算原理。在这个观点中，我们提出了浅层大脑假设：分层皮层处理与大规模并行过程相结合，皮层下区域对此做出了巨大贡献。这种浅层架构利用了典型分层深度学习和预测编码网络中不包含的皮质微电路和丘脑皮质环的计算能力。我们认为，浅层大脑结构比深层层次结构提供了几个关键优势，并且更完整地描述了哺乳动物大脑如何实现快速灵活的计算能力。