Predictive coding provides a computational paradigm for modelling perceptual processing as the construction of representations accounting for causes of sensory inputs. Here, we developed a scalable, deep network architecture for predictive coding that is trained using a gated Hebbian learning rule and mimics the feedforward and feedback connectivity of the cortex. After training on image datasets, the models formed latent representations in higher areas that allowed reconstruction of the original images. We analyzed low- and high-level properties such as orientation selectivity, object selectivity and sparseness of neuronal populations in the model. As reported experimentally, image selectivity increased systematically across ascending areas in the model hierarchy. Depending on the strength of regularization factors, sparseness also increased from lower to higher areas. The results suggest a rationale as to why experimental results on sparseness across the cortical hierarchy have been inconsistent. Finally, representations for different object classes became more distinguishable from lower to higher areas. Thus, deep neural networks trained using a gated Hebbian formulation of predictive coding can reproduce several properties associated with neuronal responses along the visual cortical hierarchy.

中文翻译：

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深门控 Hebbian 预测编码解释了沿着视觉皮层层次结构出现的复杂神经反应特性

预测编码提供了一种计算范式，用于将感知处理建模为解释感官输入原因的表示的构建。在这里，我们开发了一种用于预测编码的可扩展的深度网络架构，该架构使用门控 Hebbian 学习规则进行训练，并模拟皮层的前馈和反馈连接。在对图像数据集进行训练后，模型在更高的区域形成了潜在的表示，允许重建原始图像。我们分析了模型中神经元群的低级和高级属性，例如方向选择性、对象选择性和稀疏性。正如实验报告的那样，图像选择性在模型层次结构中的上升区域系统地增加。取决于正则化因子的强度，稀疏度也从较低的区域增加到较高的区域。结果表明了为什么关于整个皮质层次结构的稀疏性的实验结果不一致的基本原理。最后，不同对象类的表示从较低到较高的区域变得更加可区分。因此，使用门控 Hebbian 预测编码公式训练的深度神经网络可以再现与沿视觉皮层层次结构的神经元反应相关的几个特性。