A multidisciplinary approach to the study of shape and motion processing and representation in rats

During my PhD I investigated how shape and motion information are processed by the rat visual system, so as to establish how advanced is the representation of higher-order visual information in this species and, ultimately, to understand to what extent rats can present a valuable alternative to monkeys, as experimental models, in vision studies. Specifically, in my thesis work, I have investigated: 1) The possible visual strategies underlying shape recognition. 2) The ability of rat visual cortical areas to represent motion and shape information. My work contemplated two different, but complementary experimental approaches: psychophysical measurements of the rat\u2019s recognition ability and strategy, and in vivo extracellular recordings in anaesthetized animals passively exposed to various (static and moving) visual stimulation. The first approach implied training the rats to an invariant object recognition task, i.e. to tolerate different ranges of transformations in the object\u2019s appearance, and the application of an mage classification technique known as The Bubbles to reveal the visual strategy the animals were able, under different conditions of stimulus discriminability, to adopt in order to perform the task. The second approach involved electrophysiological exploration of different visual areas in the rat\u2019s cortex, in order to investigate putative functional hierarchies (or streams of processing) in the computation of motion and shape information. Results show, on one hand, that rats are able, under conditions of highly stimulus discriminability, to adopt a shape-based, view-invariant, multi-featural recognition strategy; on the other hand, the functional properties of neurons recorded from different visual areas suggest the presence of a putative shape-based, ventral-like stream of processing in the rat\u2019s visual cortex. The general purpose of my work is and has been the unveiling the neural mechanisms that make object recognition happen, with the goal of eventually 1) be able to relate my findings on rats to those on more visually-advanced species, such as human and non-human primates; and 2) collect enough biological data to support the artificial simulation of visual recognition processes, which still presents an important scientific challenge

Sparse visual models for biologically inspired sensorimotor control

Given the importance of using resources efficiently in the competition for survival, it is reasonable to think that natural evolution has discovered efficient cortical coding strategies for representing natural visual information. Sparse representations have intrinsic advantages in terms of fault-tolerance and low-power consumption potential, and can therefore be attractive for robot sensorimotor control with powerful dispositions for decision-making. Inspired by the mammalian brain and its visual ventral pathway, we present in this paper a hierarchical sparse coding network architecture that extracts visual features for use in sensorimotor control. Testing with natural images demonstrates that this sparse coding facilitates processing and learning in subsequent layers. Previous studies have shown how the responses of complex cells could be sparsely represented by a higher-order neural layer. Here we extend sparse coding in each network layer, showing that detailed modeling of earlier stages in the visual pathway enhances the characteristics of the receptive fields developed in subsequent stages. The yield network is more dynamic with richer and more biologically plausible input and output representation

Action Recognition Using Visual-Neuron Feature of Motion-Salience Region

This paper proposes a shape-based neurobiological approach for action recognition. Our work is motivated by the successful quantitative model for the organization of the shape pathways in primate visual cortex. In our approach the motion-salience region (MSR) is firstly extracted from the sequential silhouettes of an action. Then, the MSR is represented by simulating the static object representation in the ventral stream of primate visual cortex. Finally, a linear multi-class classifier is used to classify the action. Experiments on publicly available action datasets demonstrate the proposed approach is robust to partial occlusion and deformation of actors and has lower computational cost than the neurobiological models that simulate the motion representation in primate dorsal stream

Accuracy of rats in discriminating visual objects Is explained by the complexity of their perceptual strategy

Despite their growing popularity as models of visual functions, it is widely assumed that rodents deploy perceptual strategies not nearly as advanced as those of primates, when processing visual objects. Such belief is fostered by the conflicting findings about the complexity of rodent pattern vision, which appears to range from mere detection of overall object luminance to view-invariant processing of discriminant shape features. Here, we sought to clarify how refined object vision is in rodents, by measuring how well a group of rats discriminated a reference object from eleven distractors, spanning a spectrum of image-level similarity with the reference. We also presented the animals with random variations of the reference, and we processed their responses to these stimuli to obtain subject-specific models of rat perceptual choices. These models captured very well the highly variable discrimination performance observed across subjects and object conditions. In particular, they revealed how the animals that succeeded with the more challenging distractors were those that integrated the wider variety of discriminant features into their perceptual strategy. Critically, these features remained highly subject-specific and largely invariant under changes in object appearance (e.g., size variation), although they were properly reformatted (e.g., rescaled) to deal with the specific transformations the objects underwent. Overall, these findings show that rat object vision, far from being poorly developed, can be characterized as a feature-based filtering process (iterated across multiple scales, positions, etc.), similar to the one that is at work in primates and state-of-the-art machine vision systems, such as convolutional neural networks

The effects of TMS over dorsolateral prefrontal cortex on multiple visual object memory across fixation and saccades

Trans-saccadic memory, the process by which the visual system maintains the spatial position and features of objects across eye movements, is thought to be a form of visual working memory (Irwin, 1991). It has been shown that TMS over the frontal and parietal eye fields degrades trans-saccadic memory of multiple object features (Prime et al., 2008, 2010). We used a similar TMS protocol to investigate whether dorsolateral prefrontal cortex (DLPFC) is also involved in trans-saccadic memory. We predicted that performance would be disrupted similarly during either fixation or saccades. Instead, we found both task and hemisphere-dependent effects. During fixation, TMS over left DLPFC produced inconsistent effects, whereas TMS over right DLPFC reduced performance, consistent with its known role in working memory (Goldman-Rakic, 1987). In contrast, TMS over both sides of DLPFC enhanced trans-saccadic memory, suggesting a dis-inhibition of trans-saccadic processing. These results suggest that visual working memory during fixation and trans-saccadic memory may be supported by different, but interacting, neural circuits

Representation of natural movies in rat visual cortex

The neuroscientific study of mammalian vision has yielded important achievements in the last decades, but a thorough understanding is still lacking at anatomical and functional levels. From an operational perspective, this understanding would amount to creating an artificial system that reaches the performance and versatility of human vision. A first step to reach this goal requires understanding what neuroscientists call core object recognition, i.e., the rapid and largely feed-forward processing of visual information that mediates the identification and categorization of objects undergoing various identity-preserving transformations (DiCarlo et al., 2012). Electrophysiological experiments on primates have revealed that populations of neurons along the so-called ventral stream \u2013 a succession of areas running from the occipital to the temporal cortex \u2013 support core recognition, thanks to two key properties: along the pathway, neuronal responses become increasingly more selective to objects identities and increasingly more invariant to their transformations. With similar goals in mind, albeit with an engineering focus, the machine learning field has developed artificial neural networks, with feed-forward multi-layered architectures, that reach human-level performance in various object recognition tasks. Yet these artificial networks are only loosely inspired by the biological ones, they are mainly trained with supervised techniques, and perform on static images, so they fall short at providing a model for the understanding of the mammalian visual system (Kriegeskorte, 2015). Electrophysiological investigations therefore remain an important aspect in vision research. Primates are the closest species to humans, but conducting research with them became more and more difficult (for practical and ethical reasons). Over the past decade, rodents have been used as complementary models to monkeys in the study of visual processing, as they are smaller, reproduce faster, and, importantly, are more suitable for a large batch of experimental techniques. Recent physiological studies in the mouse and the rat have described successions of areas that resemble the visual pathways found in the monkey (Niell, 2011; Vermaercke et al., 2014; Tafazoli et al., 2017), while behavioral studies have shown that the rat visual system is capable of sophisticated object recognition (Zoccolan et al., 2009; Alemi-Neissi et al., 2013). Until recently, most of the vision research has focused on simple, static and parametric artificial stimuli (e.g. bars), leaving the representations of time-varying natural images (i.e. movies) little explored. Natural images are those we see during every-day life: they are characterized by high spatial and temporal correlations, i.e. they contain well-defined structures that have similar color intensities over extended areas, and that remain present in the scene over long intervals (for example, a tree trunk is all brown and doesn\u2019t disappear from moment to moment). Some have argued though that natural images are too complex and still poorly understood to allow well-controlled hypothesis-driven experiments (Rust and Movshon, 2005); others have instead stressed the fact that organisms have evolved within a natural environment and they must have adapted to process natural images in the most efficient way, hence the requirement to use this type of stimuli in vision studies (Barlow, 1961; Felsen and Dan, 2005). The theory that formalizes this hypothesis is named \u201cefficient coding\u201d. The aim of this PhD project is to investigate whether we find evidence in support of the theory in the visual cortex of rats. Specifically, we are addressing two important predictions: 1) that neural responses are increasingly persistent in time, which amounts to measuring if neurons across different areas fluctuate at different timescales in response to the same input (which would be a sign of invariance), and 2) that response distributions successively become sparser (a sign of selectivity). We recorded the neuronal activity in four rat visual areas: in order from the most medial to the most lateral, V1, LM, LI and LL. The results we found are described in two chapters. In the first one, \u201cRepresentation of natural movies in rat visual cortex\u201d, we observe a tendency towards an increase of slowness estimated with two different measures, and a decrease of sparseness across the four areas. In the second one, \u201cPopulation decoding\u201d, we are implementing a population decoding technique and show that LL neurons are better than those from other areas at maintaining a self-similar object representation over time. In the last chapter of the thesis we discuss possible implications of our findings

Revealing Connections in Object and Scene Processing Using Consecutive TMS and fMR-Adaptation

When processing the visual world, our brain must perform many computations that may occur across several regions. It is important to understand communications between regions in order to understand perceptual processes underlying processing of our environment. We sought to determine the connectivity of object and scene processing regions of the cortex, which are not fully established. In order to determine these connections repetitive transcranial magnetic stimulation (rTMS) and functional magnetic resonance-adaptation (fMR-A) were paired together. rTMS was applied to object-selective lateral occipital (LO) and scene-selective transverse occipital sulcus (TOS). Immediately after stimulation, participants underwent fMR-A, and pre- and post-TMS responses were compared. TMS disrupted remote regions revealing connections from LO and TOS to remote object and scene-selective regions in the occipital cortex. In addition, we report important neural correlates regarding the transference of object related information between modalities, from LO to outside the ventral network to parietal and frontal areas

Nonlinear Processing of Shape Information in Rat Lateral Extrastriate Cortex

In rodents, the progression of extrastriate areas located laterally to primary visual cortex (V1) has been assigned to a putative object-processing pathway (homologous to the primate ventral stream), based on anatomical considerations. Recently, we found functional support for such attribution (Tafazoli et al., 2017), by showing that this cortical progression is specialized for coding object identity despite view changes, the hallmark property of a ventral-like pathway. Here, we sought to clarify what computations are at the base of such specialization. To this aim, we performed multielectrode recordings from V1 and laterolateral area LL (at the apex of the putative ventral-like hierarchy) of male adult rats, during the presentation of drifting gratings and noise movies. We found that the extent to which neuronal responses were entrained to the phase of the gratings sharply dropped from V1 to LL, along with the quality of the receptive fields inferred through reverse correlation. Concomitantly, the tendency of neurons to respond to different oriented gratings increased, whereas the sharpness of orientation tuning declined. Critically, these trends are consistent with the nonlinear summation of visual inputs that is expected to take place along the ventral stream, according to the predictions of hierarchical models of ventral computations and a meta-analysis of the monkey literature. This suggests an intriguing homology between the mechanisms responsible for building up shape selectivity and transformation tolerance in the visual cortex of primates and rodents, reasserting the potential of the latter as models to investigate ventral stream functions at the circuitry level.SIGNIFICANCE STATEMENT Despite the growing popularity of rodents as models of visual functions, it remains unclear whether their visual cortex contains specialized modules for processing shape information. To addresses this question, we compared how neuronal tuning evolves from rat primary visual cortex (V1) to a downstream visual cortical region (area LL) that previous work has implicated in shape processing. In our experiments, LL neurons displayed a stronger tendency to respond to drifting gratings with different orientations while maintaining a sustained response across the whole duration of the drift cycle. These trends match the increased complexity of pattern selectivity and the augmented tolerance to stimulus translation found in monkey visual temporal cortex, thus revealing a homology between shape processing in rodents and primates