Victor Leboran

On the relationship between optical variability, visual saliency, and eye fixations: A computational approach

A hierarchical definition of optical variability is proposed that links physical magnitudes to visual saliency and yields a more reductionist interpretation than previous approaches. This definition is shown to be grounded on the classical efficient coding hypothesis. Moreover, we propose that a major goal of contextual adaptation mechanisms is to ensure the invariance of the behavior that the contribution of an image point to optical variability elicits in the visual system. This hypothesis and the necessary assumptions are tested through the comparison with human fixations and state-of-the-art approaches to saliency in three open access eye-tracking datasets, including one devoted to images with faces, as well as in a novel experiment using hyperspectral representations of surface reflectance. The results on faces yield a significant reduction of the potential strength of semantic influences compared to previous works. The results on hyperspectral images support the assumptions to estimate optical variability. As well, the proposed approach explains quantitative results related to a visual illusion observed for images of corners, which does not involve eye movements.

Neural Correlates of Decisions and Their Outcomes in the Ventral Premotor Cortex

Selection of the appropriate action in a changing environment involves a chain of events that goes from perception through decision to action and evaluation of the outcomes. What and where in the brain are the correlates of these events? The ventral premotor cortex (PMv) is a candidate because (1) it is involved in sensory transformations for visually guided actions and in perceptual decisions, and (2) it is connected with sensory, motor, and high-level cognitive areas related to performance monitoring. Therefore, we hypothesized that it would be the site for representing sensory perception for action and for evaluating the decision consequences. Trained monkeys were required to discriminate the orientation of two lines showed in sequence and separated by a delay. Monkeys compared the orientation of the second line with the memory trace of the first and communicated whether the second was to the left or to the right of the first. Here we show that the activity of PMv neurons reflected (1) the first stimuli and its memory trace during the delay and comparison periods, (2) its comparison with the second stimuli, including the strength of the evidence, and (3) the result of the discrimination (choice). After the monkeys reported the choice, there were neurons that only encoded the choices, others only the outcomes, and others the choices and outcomes together. The representation of task cues, decision variables, and their outcomes suggest a role of PMv as part of a supervisory network involved in shaping future behavior and in learning.

A role for the ventral premotor cortex beyond performance monitoring

Depending on the circumstances, decision making requires either comparing current sensory information with that showed recently or with that recovered from long-term memory (LTM). In both cases, to learn from past decisions and adapt future ones, memories and outcomes have to be available after the report of a decision. The ventral premotor cortex (PMv) is a good candidate for integrating memory traces and outcomes because it is involved in working-memory, decision-making, and encoding the outcomes. To test this hypothesis we recorded the extracellular unit activity while monkeys performed 2 variants of a visual discrimination task. In one task, the decision was based on the comparison of the orientation of a current stimulus with that of another stimulus recently shown. In the other task, the monkeys had to compare the current orientation of the stimulus with the correct one retrieved from LTM. Here, we report that when the task required retrieval of the stimulus and its use in the following trials, the neurons continue encoding this internal representation together with the outcomes after the monkey has emitted the motor response. However, this codification did not occur when the stimulus was shown recently and updated every trial. These results suggest that the PMv activity represents the information needed to evaluate the consequences of a decision. We interpret these results as evidence that the PMv plays a role in evaluating the outcomes that can serve to learn and thus adapt future decision to environmental demands.

Decision-Making, Behavioral Supervision and Learning: An Executive Role for the Ventral Premotor Cortex?

In order to adjust the behavioral performance in a changing environment, subjects have to monitor their evolving actions and to know whether their responses were correct or incorrect. This requires self-awareness, cognitive flexibility, working memory (WM), and decision making that frequently are impaired in psychosis. What is the neural substrate of these processes and where are these substrates located? Dysfunction of prefrontal, parietal, temporal cortices, and associated subcortical structures are known to be involved in some of these symptoms. The prefrontal–subcortical circuits have been the main focus of study while other cortical areas such as the premotor cortex have received less attention. The main focus of this review is about the evidence that the ventral premotor cortex processes both recent sensory information and that from long-term memory to decide and evaluate the behavior of previous decisions. This process may serve for learning and thus adapting future behavior to environmental demands. Therefore, dysfunction of this cortical area could be related to some cognitive neuropsychiatric disorders.

Dynamic Whitening Saliency

General dynamic scenes involve multiple rigid and flexible objects, with relative and common motion, camera induced or not. The complexity of the motion events together with their strong spatio-temporal correlations make the estimation of dynamic visual saliency a big computational challenge. In this work, we propose a computational model of saliency based on the assumption that perceptual relevant information is carried by high-order statistical structures. Through whitening, we completely remove the second-order information (correlations and variances) of the data, gaining access to the relevant information. The proposed approach is an analytically tractable and computationally simple framework which we call Dynamic Adaptive Whitening Saliency (AWS-D). For model assessment, the provided saliency maps were used to predict the fixations of human observers over six public video datasets, and also to reproduce the human behavior under certain psychophysical experiments (dynamic pop-out). The results demonstrate that AWS-D beats state-of-the-art dynamic saliency models, and suggest that the model might contain the basis to understand the key mechanisms of visual saliency. Experimental evaluation was performed using an extension to video of the well-known methodology for static images, together with a bootstrap permutation test (random label hypothesis) which yields additional information about temporal evolution of the metrics statistical significance.

Integrating prior shape models into level-set approaches

To incorporate prior shape information into a deformable model either local or global shape modeling must be carried out. Local shape modeling involves manual interaction to accumulate information on the shape variability of any object. It depends on the existence of homologous points, or landmarks, that must be unambiguously and consistently located in different specimens. Global shape modeling does not require the existence of landmarks. Global properties can be characterized using only a few parameters, and tend to be much more stable than local properties.In this work we propose a new approach that combines the benefits of local and global shape modeling in the field of level-set approaches. The method starts with local shape parameterization, which eases user interaction. Then, the shape is converted into an implicit representation which exploits the stability and compactness of global shape parameters.

A new radial symmetry measure applied to photogrammetry

This work presents a new measure for radial symmetry and an algorithm for its computation. This measure identifies radially symmetric blobs as locations with contributions from all orientations at some scale. Hence, at a given scale, radial symmetry is computed as the product of the responses of a set of even symmetric feature detectors, with different orientations. This operator presents low sensitivity to shapes lacking radial symmetry, is robust to noise, contrast changes and strong perspective distortions, and shows a narrow point spread function. A multi-resolution measure is provided, computed as the maximum of the symmetry measure evaluated over a set of scales. We have applied this measure in the field of photogrammetry for the detection of circular coded fiducial targets. The detection of local maxima of multi-resolution radial symmetry is combined with a step of false-positive rejection, based on elliptical model fitting. In our experiments, the efficiency of target detection with this method is improved regarding a well-known commercial system, which is expected to improve the performance of bundle adjustment techniques. In order to fulfill all steps previous to bundle adjustment, we have also developed our own method for recognition of coded targets. This is accomplished by a standard procedure of segmentation and decoding of the ring sequence. Nevertheless, we have included a step for the verification of false positives of decoding based on correlation with reference targets. As far as we know, this approach cannot be found in literature.

Analysis of the temperature dependence of the thermal conductivity of insulating single crystal oxides

The temperature dependence of the thermal conductivity of 27 different single crystal oxides is reported from ≈20 K to 350 K. These crystals have been selected among the most common substrates for growing epitaxial thin-film oxides, spanning over a range of lattice parameters from ≈3.7 A to ≈12.5 A. Different contributions to the phonon relaxation time are discussed on the basis of the Debye model. This work provides a database for the selection of appropriate substrates for thin-film growth according to their desired thermal properties, for applications in which heat management is important.

Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.

It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision-making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task.

Application of Penalized Splines in Analyzing Neuronal Data

Neuron experiments produce high-dimensional data structures. Therefore, application of smoothing techniques in the analysis of neuronal data from electrophysiological experiments has received considerable attention of late. We investigate the use of penalized splines in the analysis of neuronal data. This is first illustrated when interested in the temporal trend of a single neuron. An approach to investigate the maximal firing rate, based on the penalizedspline model is proposed. Determination of the time of maximal firing rate is based on non-linear optimization of the objective function with the corresponding confidence intervals constructed based on the first-order derivative function. To distinguish between the curves from different experimental conditions in a moment-by-moment sense, bias adjusted simulation-based simultaneous confidence bands leading to global inference in the time domain are constructed. The bands are an extension of the approach proposed by Ruppert et al. (2003). These methods are in a second step extended towards the analysis of a population of neurons via a marginal or population-averaged model.