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The article explores the nature of high-level visual neurons, known as "face cells," in the ventral stream, which respond more to images of faces than other objects. It investigates whether the selectivity of these face cells arises from face-specific features or whether they are part of a broader object space where their tuning is based on discriminative visuo-statistical properties.

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]

Repetition suppression, which refers to reduced neural activity for repeated stimuli, is typically explained by bottom-up or local adaptation mechanisms. However, recent theories have emphasized the role of top-down processes, suggesting that this response reduction reflects the fulfillment of perceptual expectations. To support this, an influential human fMRI study showed that the magnitude of suppression is modulated by the probability of a repetition. No such repetition probability effect was found in macaque inferior temporal (IT) cortex for spiking activity despite the presence of repetition suppression. Contrary to the human fMRI studies that showed an effect of repetition probability, the macaque single-unit study used a large variety of unfamiliar stimuli and the monkeys were not required to attend the stimuli. Here, as in the human fMRI studies, we used faces as stimuli and made the monkeys attend to the stimulus content.We simultaneously recorded spiking activity and local field potentials (LFPs) in the middle lateral face patch (ML) of one monkey (male) and a face-responsive region of another (female). Although we observed significant repetition suppression of spiking activity and high gamma-band LFPs in both animals, there were no effects of repetition probability even when repetitions were task relevant and repetition probability affected behavioral decisions. In conclusion, despite the use of face stimuli and a stimulus-related task, no neural signature of repetition probability was present for faces in a face responsive patch of macaque IT. This further challenges a general perceptual expectation account of repetition suppression. [ABSTRACT FROM AUTHOR]

Summary From an ecological point of view, it is generally suggested that the main goal of vision in rats and mice is navigation and (aerial) predator evasion [ 1–3 ]. The latter requires fast and accurate detection of a change in the visual environment. An outstanding question is whether there are mechanisms in the rodent visual system that would support and facilitate visual change detection. An experimental protocol frequently used to investigate change detection in humans is the oddball paradigm, in which a rare, unexpected stimulus is presented in a train of stimulus repetitions [ 4 ]. A popular “predictive coding” theory of cortical responses states that neural responses should decrease for expected sensory input and increase for unexpected input [ 5, 6 ]. Despite evidence for response suppression and enhancement in noninvasive scalp recordings in humans with this paradigm [ 7, 8 ], it has proven challenging to observe both phenomena in invasive action potential recordings in other animals [ 9–11 ]. During a visual oddball experiment, we recorded multi-unit spiking activity in rat primary visual cortex (V1) and latero-intermediate area (LI), which is a higher area of the rodent ventral visual stream. In rat V1, there was only evidence for response suppression related to stimulus-specific adaptation, and not for response enhancement. However, higher up in area LI, spiking activity showed clear surprise-based response enhancement in addition to stimulus-specific adaptation. These results show that neural responses along the rat ventral visual stream become increasingly sensitive to changes in the visual environment, suggesting a system specialized in the detection of unexpected events. [ABSTRACT FROM AUTHOR]

Visual categorization of complex, natural stimuli has been studied for some time in human and nonhuman primates. Recent interest in the rodent as a model for visual perception, including higher-level functional specialization, leads to the question of how rodents would perform on a categorization task using natural stimuli. To answer this question, rats were trained in a two-alternative forced choice task to discriminate movies containing rats from movies containing other objects and from scrambled movies (ordinate-level categorization). Subsequently, transfer to novel, previously unseen stimuli was tested, followed by a series of control probes. The results show that the animals are capable of acquiring a decision rule by abstracting common features from natural movies to generalize categorization to new stimuli. Control probes demonstrate that they did not use single low-level features, such as motion energy or (local) luminance. Significant generalization was even present with stationary snapshots from untrained movies. The variability within and between training and test stimuli, the complexity of natural movies, and the control experiments and analyses all suggest that a more high-level rule based on more complex stimulus features than local luminance-based cues was used to classify the novel stimuli. In conclusion, natural stimuli can be used to probe ordinate-level categorization in rats. [ABSTRACT FROM AUTHOR]