Sussex AI Group, Department of Informatics, University of Sussex, Brighton, United Kingdom.
VERSES Research Lab, Los Angeles, California, United States of America.
PLoS Comput Biol. 2023 Aug 2;19(8):e1011280. doi: 10.1371/journal.pcbi.1011280. eCollection 2023 Aug.
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 successful 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 (applying a learned function that maps directly from data to beliefs) and recurrent processing can be understood as performing iterative inference (sequentially updating neural activity in order to improve the accuracy of beliefs). 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.
预测编码是一种有影响力的皮质神经活动模型。它提出，知觉信念是通过依次最小化“预测误差”（预测数据与观察数据之间的差异）来提供的。这一建议隐含的意思是，成功的感知需要多次神经活动循环。这与以下证据相矛盾：视觉感知的几个方面——包括复杂形式的物体识别——来自于快速时间尺度上发生的初始“前馈扫描”，这排除了大量的递归活动。在这里，我们提出前馈扫描可以被理解为执行摊销推断（应用直接从数据映射到信念的学习函数），而递归处理可以被理解为执行迭代推断（为了提高信念的准确性，顺序更新神经活动）。我们提出了一种混合预测编码网络，通过用单个目标函数的双重优化来描述迭代和摊销推断，以一种有原则的方式将两者结合起来。我们表明，所得到的方案可以在一种生物上合理的神经结构中实现，该结构利用局部赫布更新规则来近似贝叶斯推理。我们证明，我们的混合预测编码模型结合了摊销和迭代推断的优点——对于熟悉的数据，获得快速且计算成本低廉的感知推断，同时保持迭代推断方案的上下文敏感性、精度和样本效率。此外，我们展示了我们的模型如何对其不确定性敏感，并自适应地平衡迭代和摊销推断，以使用最小的计算费用获得准确的信念。混合预测编码为视觉感知过程中观察到的前馈和递归活动的功能相关性提供了一个新的视角，并为视觉现象学的不同方面提供了新的见解。
