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.

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.

In recent years, the rodent has come forward as a candidate model for investigating higher level visual abilities such as object vision. This view has been backed up substantially by evidence from behavioral studies that show rats can be trained to express visual object recognition and categorization capabilities. However, almost no studies have investigated the functional properties of rodent extrastriate visual cortex using stimuli that target object vision, leaving a gap compared with the primate literature. Therefore, we recorded single-neuron responses along a proposed ventral pathway in rat visual cortex to investigate hallmarks of primate neural object representations such as preference for intact versus scrambled stimuli and category-selectivity. We presented natural movies containing a rat or no rat as well as their phase-scrambled versions. Population analyses showed increased dissociation in representations of natural versus scrambled stimuli along the targeted stream, but without a clear preference for natural stimuli. Along the measured cortical hierarchy the neural response seemed to be driven increasingly by features that are not V1-like and destroyed by phase-scrambling. However, there was no evidence for category selectivity for the rat versus nonrat distinction. Together, these findings provide insights about differences and commonalities between rodent and primate visual cortex.

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.

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. Repetition suppression is a reduced brain activity for repeated stimuli commonly observed across species. In the predictive coding framework, such suppression is thought to reflect fulfilled perceptual expectations. Although this hypothesis is supported by several human fMRI studies reporting an effect of repetition probability on repetition suppression, this could not be replicated in single-cell recordings in monkey inferior temporal (IT) cortex. Subsequent studies narrowed down the conditions for the effect to requiring attention and being limited to particular stimulus categories such as faces. Here, we show that, even under these conditions, repetition suppression in monkey IT neurons is still unaffected by repetition probability, even in a task with a behavioral effect, challenging the perceptual expectation account of repetition suppression.