Predictive coding is an influential model of cortical neural activity. It
proposes that perceptual beliefs are furnished by sequentially minimising
"prediction errors" - the differences between predicted and observed data.
Implicit in this proposal is the idea that perception requires multiple cycles
of neural activity. This is at odds with evidence that several aspects of
visual perception - including complex forms of object recognition - arise from
an initial "feedforward sweep" that occurs on fast timescales which preclude
substantial recurrent activity. Here, we propose that the feedforward sweep can
be understood as performing amortized inference and recurrent processing can be
understood as performing iterative inference. We propose a hybrid predictive
coding network that combines both iterative and amortized inference in a
principled manner by describing both in terms of a dual optimization of a
single objective function. We show that the resulting scheme can be implemented
in a biologically plausible neural architecture that approximates Bayesian
inference utilising local Hebbian update rules. We demonstrate that our hybrid
predictive coding model combines the benefits of both amortized and iterative
inference -- obtaining rapid and computationally cheap perceptual inference for
familiar data while maintaining the context-sensitivity, precision, and sample
efficiency of iterative inference schemes. Moreover, we show how our model is
inherently sensitive to its uncertainty and adaptively balances iterative and
amortized inference to obtain accurate beliefs using minimum computational
expense. Hybrid predictive coding offers a new perspective on the functional
relevance of the feedforward and recurrent activity observed during visual
perception and offers novel insights into distinct aspects of visual
phenomenology.

提出了一种混合的预测编码网络，通过描述单个目标函数的双重优化，以原则性地将迭代和摊销推理结合起来，并在在仿生神经结构中实现，将受益于摊销和迭代推理的优势，同时保持迭代推理方案的上下文敏感性，精度和采样效率，在视觉感知中提供了新的功能相关性视角，并提供了视觉现象的不同方面的新见解。