The role of posterior ventral stream areas for viewpoint-invariant object recognition

Object constancy is one of the most crucial mechanisms of the human visual system enabling viewpoint invariant object recognition. However, the neuronal foundations of object constancy are widely unknown. Research has shown that the ventral visual stream is involved in processing of various kinds of object stimuli and that several regions along the ventral stream are possibly sensitive to the orientation of an object in space. To systematically address the question of viewpoint sensitive object perception, we conducted a study with stroke patients as well as an fMRI experiment with healthy participants applying object stimuli in several spatial orientations, for example in typical and atypical viewing conditions. In the fMRI experiment, we found stronger BOLD signals and above-chance classification accuracies for objects presented in atypical viewing conditions in fusiform face sensitive and lateral occipito-temporal object preferring areas. In the behavioral patient study, we observed that lesions of the right fusiform gyrus were associated with lower performance in object recognition for atypical views. The complementary results from both experiments emphasize the contributions of fusiform and lateral-occipital areas to visual object constancy and indicate that visual object constancy is particularly enabled through increased neuronal activity and specific activation patterns for objects in demanding viewing conditions.

TARGET RECOGNITION BASED ON CENTRAL AND LOGARITHMIC CENTRAL IMAGE CHORD TRANSFORMATIONS

Object recognition is of great importance for many civil and military applications and supposes the identification and classification of the object in real-time, regardless of spatial position, angular orientation, etc. In the article, the methods of central and logarithmic central image chord transformations are described. The system for invariant object recognition and space parameters determination was developed based on the proposed image chord transformation operations, which is of a multiprocessor functionally distributed architecture, in which various modules implement separate image processing operations, and allows to recognize the objects regardless of their position, scale, or rotation, and to determine the parameters for targeting The model of a data processing flow in the system was elaborated to permit the determination of the signal processing stages which can be realized in parallel. An analytical estimation and analysis of the system’s data processing time and throughput is carrying out. The approach of the system’s optimization was applied and allows to decrease the total data processing time by 24.5 times and increase the system’s throughput by 75 times.

CENTRAL AND LOGARITHMIC CENTRAL IMAGE CHORD TRANSFORMATIONS FOR INVARIANT OBJECT RECOGNITION

Pattern descriptors invariant to rotation, scaling, and translation represents an important direction in the elaboration of the real time object recognition systems. In this article, the new kinds of object descriptors based on chord transformation are presented. There are described new methods of image presentation - Central and Logarithmic Central Image Chord Transformations (CICT and LCICT). It is shown that the CICToperation makes it possible to achieve invariance to object rotation. In the case of implementation of the LCICT transformation, invariance to changes in the rotation and scale of the object is achieved. The possibilities of implementing the CICTand LCICToperations are discussed. The algorithms of these operations for contour images are presented. The possibilities of integrated implementation of CICT and LCICT operations are considered. A generalized CICT operation for a full (halftone) image is defined. The structures of the coherent optical processors that implement operations of basic and integral image chord transformations are presented.

Using deep neural networks to evaluate object vision tasks in rats

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.

Marmoset invariant object recognition behavior mirrors that of humans and macaques at fine scale

The marmoset—a small monkey with a flat cortex—offers powerful techniques for studying neural circuits in a primate. However, it remains unclear whether brain functions typically studied in larger primates can be studied in the marmoset. Here, we asked whether the 300-gram marmosets’ perceptual and cognitive repertoire approaches human levels or is instead closer to rodents’. Using high-level visual object recognition as a testbed, we found that on the same task marmosets substantially outperformed rats and generalized far more robustly across images, all while performing ∼1000 trials/day. We then compared marmosets against the high standard of human behavior. Across the same 400 images, marmosets’ image-by-image recognition behavior was strikingly human-like—essentially as human-like as macaques’. These results demonstrate that marmosets have been substantially underestimated and that high-level abilities have been conserved across simian primates. Consequently, marmosets are a potent small model organism for visual neuroscience, and perhaps beyond.

A non-canonical feedforward pathway for computing odor identity

Sensory systems rely on statistical regularities in the experienced inputs to either group disparate stimuli, or parse them into separate categories1,2. While considerable progress has been made in understanding invariant object recognition in the visual system3–5, how this is implemented by olfactory neural circuits remains an open question6–10. The current leading model states that odor identity is primarily computed in the piriform cortex, drawing from mitral cell (MC) input6–9,11. Surprisingly, the role of tufted cells (TC)12–16, the other principal cell-type of the olfactory bulb (OB) in decoding odor identity, and their dependence on cortical feedback, has been overlooked. Tufted cells preferentially project to the anterior olfactory nucleus (AON) and olfactory striatum, while mitral cells strongly innervate the piriform cortex (PC). Here we show that classifiers based on the population activity of tufted cells successfully decode both odor identity and intensity across a large concentration range. In these computations, tufted cells substantially outperform mitral cells, and are largely unaffected by silencing of cortical feedback. Further, cortical feedback from AON controls preferentially the gain of tufted cell odor representations, while PC feedback specifically restructures mitral cell responses, matching biases in feedforward connectivity. Leveraging cell-type specific analyses, we identify a non-canonical feedforward pathway for odor recognition and discrimination mediated by the tufted cells, and propose that OB target areas, other than the piriform cortex, such as AON and olfactory striatum, are well-positioned to compute odor identity.

Object manifold geometry across the mouse cortical visual hierarchy

Despite variations in appearance we robustly recognize objects. Neuronal populations responding to objects presented under varying conditions form object manifolds and hierarchically organized visual areas are thought to untangle pixel intensities into linearly decodable object representations. However, the associated changes in the geometry of object manifolds along the cortex remain unknown. Using home cage training we showed that mice are capable of invariant object recognition. We simultaneously recorded the responses of thousands of neurons to measure the information about object identity available across the visual cortex and found that lateral visual areas LM, LI and AL carry more linearly decodable object identity information compared to other visual areas. We applied the theory of linear separability of manifolds, and found that the increase in classification capacity is associated with a decrease in the dimension and radius of the object manifold, identifying features of the population code that enable invariant object coding.

Deep Neural Networks Point to Mid-level Complexity of Rodent Object Vision

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 high-profile papers 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 the richness of information processing in primates through a succession of convolutional and fully connected layers. We find that rodent object vision 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 complex 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.

Intrinsic dimension estimation for locally undersampled data

Identifying the minimal number of parameters needed to describe a dataset is a challenging problem known in the literature as intrinsic dimension estimation. All the existing intrinsic dimension estimators are not reliable whenever the dataset is locally undersampled, and this is at the core of the so called curse of dimensionality. Here we introduce a new intrinsic dimension estimator that leverages on simple properties of the tangent space of a manifold and extends the usual correlation integral estimator to alleviate the extreme undersampling problem. Based on this insight, we explore a multiscale generalization of the algorithm that is capable of (i) identifying multiple dimensionalities in a dataset, and (ii) providing accurate estimates of the intrinsic dimension of extremely curved manifolds. We test the method on manifolds generated from global transformations of high-contrast images, relevant for invariant object recognition and considered a challenge for state-of-the-art intrinsic dimension estimators.