Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

Abstract

The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of “kernel analysis” that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

Author Summary

Primates are remarkable at determining the category of a visually presented object even in brief presentations, and under changes to object exemplar, position, pose, scale, and background. To date, this behavior has been unmatched by artificial computational systems. However, the field of machine learning has made great strides in producing artificial deep neural network systems that perform highly on object recognition benchmarks. In this study, we measured the responses of neural populations in inferior temporal (IT) cortex across thousands of images and compared the performance of neural features to features derived from the latest deep neural networks. Remarkably, we found that the latest artificial deep neural networks achieve performance equal to the performance of IT cortex. Both deep neural networks and IT cortex create representational spaces in which images with objects of the same category are close, and images with objects of different categories are far apart, even in the presence of large variations in object exemplar, position, pose, scale, and background. Furthermore, we show that the top-level features in these models exceed previous models in predicting the IT neural responses themselves. This result indicates that the latest deep neural networks may provide insight into understanding primate visual processing.