Davide Zoccolan

Recent studies have revealed that human and non-human animals (rats) can detect luminance distribution and correlations between pixels in an image (ranging from 2-point to 4-point). This sensitivity is believed to stem from optimization processes in the visual system operating through efficient coding mechanisms that retain the most informative and significant features (here identified as the most variable correlations), thereby reducing costs to extract biologically relevant information from the environment. However, it is yet to be determined whether this optimization is evolutionarily given by inborn mechanisms or shaped by visual experience. Here we report that newly hatched visually naive domestic chicks (Galluls gallus) spontaneously prefer to approach luminance, 2-point and 4-point correlation patterns (respectively, horizontal lines and rectangular patterns), while showing no preference for 3-point correlation over noise controls. This parallels the ranking observed in adult humans and rats, thus suggesting that evolutionarily given biological predispositions largely drive efficient coding of natural images. We also found that learning by exposure to visual stimuli, as occurs naturally during visual imprinting, induced a preference for noise over point correlation patterns in chicks exposed to 3- and 4-point patterns. We hypothesize that this behaviour could reflect chicks’ preference for stimuli of lower statistical (correlation-based) complexity.

The brain combines information from multiple sensory modalities to build a consistent representation of the world. The principles by which multimodal stimuli are integrated in cortical hierarchies are well studied, but it is less clear whether and how unimodal inputs shape the processing of signals carried by a different modality. In rodents, for instance, direct connections from primary auditory cortex reach visual cortex, but studies disagree on the impact of these projections on visual cortical processing. Both enhancement and suppression of visually evoked responses by auditory inputs have been reported, as well as sharpening of orientation tuning and improvement in the coding of visual information. Little is known, however, about the functional impact of auditory signals on rodent visual perception. Here we trained a group of rats in a visual temporal frequency (TF) classification task, where the visual stimuli to categorize were paired with simultaneous but task-irrelevant auditory stimuli, to prevent high-level multisensory integration and investigate instead the spontaneous, direct impact of auditory signals on the perception of visual stimuli. Rat classification of visual TF was strongly and systematically altered by the presence of sounds, in a way that was determined by sound intensity but not by its temporal modulation. To investigate the mechanisms underlying this phenomenon, we developed a Bayesian ideal observer model, combined with a neural coding scheme where neurons linearly encode visual TF but are inhibited by concomitant sounds by a measure that depends on their intensity. This model captured very precisely the full spectrum of rat perceptual choices we observed, supporting the hypothesis that auditory inputs induce an effective compression of the visual perceptual space. This suggests an important role for inhibition as the key mediator of auditory-visual interactions and provides clear, mechanistic hypotheses to be tested by future work on visual cortical codes.

Sensory processing in the cortex follows a hierarchical structure, transforming fine-grained stimulus features into abstract, invariant representations. These transformations have typically been characterised in visual pathways ranging from primary visual cortex to inferotemporal cortex. Less is known about how stimulus representations are formatted in other modalities and downstream regions such as the perirhinal cortex (PER), which receives processed sensory input from all modalities and is involved in object recognition. To address this, we investigated neural activity along the somatosensory hierarchy in awake, head-fixed mice receiving tactile whisker stimulation on either side of the face. PCA and stimulus-baseline decoding analyses revealed that PER activity was similar across stimulated sides, while this was not the case in primary somatosensory cortex (wS1). We also performed decoding analyses for the side of stimulation and the stimulus’ movement direction and found that wS1 and secondary somatosensory cortex could reliably decode both features. In contrast, PER was shown to be invariant to both features. We also clustered neurons based on their response dynamics, which indicated that the majority of PER neurons did not encode the basic stimulus features. These findings demonstrate that PER is a critical node in the somatosensory processing hierarchy, encoding tactile information in an invariant and abstract format. Together, these insights contribute to a more refined understanding of how perceptual processing in PER contributes to the various cognitive processes it is utilised for. Significance Statement: Sensory information is processed in stages that transform detailed signals into complex, stable representations. The perirhinal cortex (PER) is at the apex of these stages and is involved in recognizing objects, a cognitive process that requires both perceptual and memory-based processing. While PER’s role in the latter is established, evidence for the former is conflicting. To investigate this, we recorded PER activity in mice passively experiencing simple tactile stimulation with two pairs of feature combinations. Along the cortical somatosensory hierarchy invariance of stimulus representation emerged in PER, while individual features were represented in earlier cortices with high fidelity. This suggests that PER contributes to the formation of invariant representations of sensory stimuli that is a foundation for object recognition.

The brain combines information from multiple sensory modalities to build a consistent view of the world. The principles by which multimodal stimuli are integrated in cortical hierarchies are well studied, but it is less clear whether and how unimodal inputs systematically shape the processing of signals carried by a different modality. Here we use a visual classification task in rats to investigate how task-irrelevant sounds modify the processing of visual stimuli. Our data shows that the intensity of a sound, but not its temporal modulation frequency, enacts a powerful, effective compression of the visual perceptual space. This result underscores the importance of cross-modal influences in perceptual pathways and suggests an important role for inhibition as the mediator of auditory-visual interactions at the neural representation level.

Despite their prominence as model systems to dissect visual cortical circuitry, it remains unclear whether rodents are capable of truly advanced processing of visual information. Here, we considered several psychophysical studies of rat object vision, and we used a deep convolutional neural network (CNN) to measure the computational complexity required to account for the patterns of rat performances reported in these studies, as well as for the animals’ perceptual strategies. We found that at least half of the CNN depth was required to match the modulation of rat classification accuracy in tasks where objects underwent variations of size, position and orientation. However, the full network was needed to equal the tolerance of rat perception to more severe image manipulations, such as partial occlusion and reduction of objects to their outlines. Finally, rats displayed a perceptual strategy that was way more invariant than that of the CNN, as they more consistently relied on the same set of diagnostic features across object transformations. Overall, these results reveal an unexpected level of sophistication of rat object vision, while reinforcing the intuition that, despite their proficiency in solving challenging image classification tasks, CNNs learn solutions that only marginally match those of biological visual systems.

A key feature of advanced motion processing in the primate dorsal stream is the existence of pattern cells—specialized cortical neurons that integrate local motion signals into pattern-invariant representations of global direction. Pattern cells have also been reported in rodent visual cortex, but it is unknown whether the tuning of these neurons results from truly integrative, nonlinear mechanisms or trivially arises from linear receptive fields (RFs) with a peculiar geometry. Here, we show that pattern cells in rat primary (V1) and lateromedial (LM) visual cortex process motion direction in a way that cannot be explained by the linear spatiotemporal structure of their RFs. Instead, their tuning properties are consistent with and well explained by those of units in a state-of-the-art neural network model of the dorsal stream. This suggests that similar cortical processes underlay motion representation in primates and rodents. The latter could thus serve as powerful model systems to unravel the underlying circuit-level mechanisms.

The defining feature of advanced motion processing in the primate dorsal stream is the existence of pattern cells – specialized cortical neurons that integrate local motion signals into pattern-invariant representations of global direction. Pattern cells have also been reported in rodent visual cortex, but it is unknown whether the tuning of these neurons results from truly integrative, nonlinear mechanisms or trivially arises from linear receptive fields (RFs) with a peculiar geometry. Here we show that pattern cells in rat visual cortical areas V1 and LM process motion direction in a way that cannot be explained by the linear spatiotemporal structure of their RFs. Instead, their tuning properties are consistent with those of units in a state-of-the-art neural network model of the dorsal stream. This suggests that similar cortical processes underly motion representation in primates and rodents. The latter could thus serve as powerful model systems to unravel the underlying circuit-level mechanisms.

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al., 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from two- to four-point correlations and a further decrease from four- to three-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.