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Abstract：
Key properties of inferior temporal cortex neurons are described, and then, the biological plausibility of two leading approaches to invariant visual object recognition in the ventral visual system is assessed to investigate whether they account for these properties. Experiment 1 shows that VisNet performs object classification with random exemplars comparably to HMAX, except that the final layer C neurons of HMAX have a very non-sparse representation (unlike that in the brain) that provides little information in the single-neuron responses about the object class. Experiment 2 shows that VisNet forms invariant representations when trained with different views of each object, whereas HMAX performs poorly when assessed with a biologically plausible pattern association network, as HMAX has no mechanism to learn view invariance. Experiment 3 shows that VisNet neurons do not respond to scrambled images of faces, and thus encode shape information. HMAX neurons responded with similarly high rates to the unscrambled and scrambled faces, indicating that low-level features including texture may be relevant to HMAX performance. Experiment 4 shows that VisNet can learn to recognize objects even when the view provided by the object changes catastrophically as it transforms, whereas HMAX has no learning mechanism in its S-C hierarchy that provides for view-invariant learning. This highlights some requirements for the neurobiological mechanisms of high-level vision, and how some different approaches perform, in order to help understand the fundamental underlying principles of invariant visual object recognition in the ventral visual stream.
摘要：
描述了颞下皮层神经元的关键特性，然后,评估了在腹侧视觉系统中进行不变视觉物体识别的两种主要方法的生物学合理性，以调查它们是否解释了这些特性。实验1表明，与HMAX相比，VisNet使用随机样本执行对象分类。除了HMAX的最终C层神经元具有非常非稀疏的表示（与大脑中的表示不同），该表示在有关对象类的单神经元响应中提供了很少的信息。实验2表明，当用每个对象的不同视图训练时，VisNet形成不变表示，而HMAX在使用生物学上似是而非的模式关联网络进行评估时表现不佳，因为HMAX没有学习视图不变性的机制。实验3表明，VisNet神经元对人脸的乱序图像没有反应，从而对形状信息进行编码。HMAX神经元对未打乱和打乱的面孔做出了同样高的反应，表明包括纹理在内的低级特征可能与HMAX性能相关。实验4表明，即使对象提供的视图在转换时发生灾难性的变化，VisNet也可以学习识别对象，而HMAX在其S-C层次结构中没有提供视图不变学习的学习机制。这突出了对高级视觉的神经生物学机制的一些要求，以及一些不同的方法是如何执行的，以帮助理解腹侧视觉流中不变视觉对象识别的基本原理。
