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In the last two decades rodents have been on the rise as a dominant model for visual neuroscience. This is particularly true for earlier levels of information processing, but a number of studies have suggested that also higher levels of processing such as invariant object recognition occur in rodents. Here we provide a quantitative and comprehensive assessment of this claim by comparing a wide range of rodent behavioral and neural data with convolutional deep neural networks. These networks have been shown to capture hallmark properties of information processing in primates through a succession of convolutional and fully connected layers. We find that performance rodent object vision tasks can be captured using low to mid-level convolutional layers only, without any convincing evidence for the need of higher layers known to simulate complex object recognition in primates. Our approach also reveals surprising insights on assumptions made before, for example, that the best performing animals would be the ones using the most abstract representations–which we show to likely be incorrect. Our findings suggest a road ahead for further studies aiming at quantifying and establishing the richness of representations underlying information processing in animal models at large. Author summary: Despite years of investigating object recognition in rodents, it remains unclear to what extent their visual system supports a capacity for high-level, abstract representations. Here, we used computational deep neural network models to assess which level of abstraction is required to reproduce rodent behavior in several studies, and which level matches representations in higher rodent visual areas. We found that both behavioral and neural data support mid-level representations at best, and show that certain evidence available in the literature may not provide as strong support for invariant visual object recognition as previously thought. Going forward, our findings suggest that computational models could serve as a principled benchmark for evaluating the richness of information processing across species and for designing experiments to push the boundaries of animal models. [ABSTRACT FROM AUTHOR]

Computer vision models, Ventral visual cortex, intermediate representations, visual deficits Responses in macaque visual area V4 following inactivation of the parvocellular and magnocellular LGN pathways. Keywords: Ventral visual cortex; intermediate representations; computer vision models; visual deficits EN Ventral visual cortex intermediate representations computer vision models visual deficits 75 77 3 09/16/22 20220201 NES 220201 A fundamental task of the human visual cortex is to rapidly and accurately recognize objects in the environment, a task that requires disentangling shape and identity information from incidental features such as size, position, or orientation. A long-standing hypothesis posits that the ventral visual cortex achieves invariant object recognition by transforming the visual input to encode object shape information independently from size, position, etc. [Extracted from the article]

Recent studies suggest that deep Convolutional Neural Network (CNN) models show higher representational similarity, compared to any other existing object recognition models, with macaque inferior temporal (IT) cortical responses, human ventral stream fMRI activations and human object recognition. These studies employed natural images of objects. A long research tradition employed abstract shapes to probe the selectivity of IT neurons. If CNN models provide a realistic model of IT responses, then they should capture the IT selectivity for such shapes. Here, we compare the activations of CNN units to a stimulus set of 2D regular and irregular shapes with the response selectivity of macaque IT neurons and with human similarity judgements. The shape set consisted of regular shapes that differed in nonaccidental properties, and irregular, asymmetrical shapes with curved or straight boundaries. We found that deep CNNs (Alexnet, VGG-16 and VGG-19) that were trained to classify natural images show response modulations to these shapes that were similar to those of IT neurons. Untrained CNNs with the same architecture than trained CNNs, but with random weights, demonstrated a poorer similarity than CNNs trained in classification. The difference between the trained and untrained CNNs emerged at the deep convolutional layers, where the similarity between the shape-related response modulations of IT neurons and the trained CNNs was high. Unlike IT neurons, human similarity judgements of the same shapes correlated best with the last layers of the trained CNNs. In particular, these deepest layers showed an enhanced sensitivity for straight versus curved irregular shapes, similar to that shown in human shape judgments. In conclusion, the representations of abstract shape similarity are highly comparable between macaque IT neurons and deep convolutional layers of CNNs that were trained to classify natural images, while human shape similarity judgments correlate better with the deepest layers. [ABSTRACT FROM AUTHOR]