Models for Motion Perception

As observers move through the environment or shift their direction of gaze, the world moves past them. In addition, there may be objects that are moving differently from the static background, either rigid-body motions or nonrigid (e.g., turbulent) ones. This dissertation discusses several models for motion perception. The models rely on first measuring motion energy, a multi-resolution representation of motion information extracted from image sequences. The image flow model combines the outputs of a set of spatiotemporal motion-energy filters to estimate image velocity, consonant with current views regarding the neurophysiology and psychophysics of motion perception. A parallel implementation computes a distributed representation of image velocity that encodes both a velocity estimate and the uncertainty in that estimate. In addition, a numerical measure of image-flow uncertainty is derived. The egomotion model poses the detection of moving objects and the recovery of depth from motion as sensor fusion problems that necessitate combining information from different sensors in the presence of noise and uncertainty. Image sequences are segmented by finding image regions corresponding to entire objects that are moving differently from the stationary background. The turbulent flow model utilizes a fractal-based model of turbulence, and estimates the fractal scaling parameter of fractal image sequences from the outputs of motion-energy filters. Some preliminary results demonstrate the model\u27s potential for discriminating image regions based on fractal scaling

Neuronal Basis of the Motion Aftereffect Reconsidered

AbstractSeveral fMRI studies have reported MT+ response increases correlated with perception of the motion aftereffect (MAE). However, attention can strongly affect MT+ responses, and subjects may naturally attend more to the MAE than control trials without MAE. We found that requiring subjects to attend to motion on both MAE and control trials produced equal levels of MT+ response, suggesting that attention may have confounded the interpretation of previous experiments; in our data, attention accounts for the entire effect. After eliminating this confound, we observed that direction-selective motion adaptation produced a direction-selective imbalance in MT+ responses (and earlier visual areas), and yielded a corresponding asymmetry in speed discrimination thresholds. These findings provide physiological evidence that population level response imbalances underlie the MAE, and quantify the relative proportions of direction-selective neurons across human visual areas

Feature-based attention enhances performance by increasing response gain

AbstractCovert spatial attention can increase contrast sensitivity either by changes in contrast gain or by changes in response gain, depending on the size of the attention field and the size of the stimulus (Herrmann et al., 2010), as predicted by the normalization model of attention (Reynolds & Heeger, 2009). For feature-based attention, unlike spatial attention, the model predicts only changes in response gain, regardless of whether the featural extent of the attention field is small or large. To test this prediction, we measured the contrast dependence of feature-based attention. Observers performed an orientation-discrimination task on a spatial array of grating patches. The spatial locations of the gratings were varied randomly so that observers could not attend to specific locations. Feature-based attention was manipulated with a 75% valid and 25% invalid pre-cue, and the featural extent of the attention field was manipulated by introducing uncertainty about the upcoming grating orientation. Performance accuracy was better for valid than for invalid pre-cues, consistent with a change in response gain, when the featural extent of the attention field was small (low uncertainty) or when it was large (high uncertainty) relative to the featural extent of the stimulus. These results for feature-based attention clearly differ from results of analogous experiments with spatial attention, yet both support key predictions of the normalization model of attention

Наближення мультифрактальних процесів і полів абсолютно неперервними процесами

Визначено абсолютно неперервні процеси, які збігаються до мультифрактального броунівського руху за ймовірністю в просторах типу Бєсова. Одержано результат про збіжність розв'язків стохастичних диференціальних рівнянь з такими процесами до розв'язку рівняння з мультифрактальним броунівським рухом. Аналогічні наближення побудовано для випадку двопараметричних процесів.We define absolute continuous stochastic processes that converge to a multifractal Brownian motion in Besov-type spaces. The convergence of solutions of stochastic differential equations with such processes to a solution of the equation with multifractal Brownian motion is proved. Similar approximations are constructed in the case of two-parameter processes

Heavy Fermion Quantum Criticality

During the last few years, investigations of Rare-Earth materials have made clear that not only the heavy fermion phase in these systems provides interesting physics, but the quantum criticality where such a phase dies exhibits novel phase transition physics not fully understood. Moreover, attempts to study the critical point numerically face the infamous fermion sign problem, which limits their accuracy. Effective action techniques and Callan-Symanzik equations have been very popular in high energy physics, where they enjoy a good record of success. Yet, they have been little exploited for fermionic systems in condensed matter physics. In this work, we apply the RG effective action and Callan-Symanzik techiques to the heavy fermion problem. We write for the first time the effective action describing the low energy physics of the system. The f-fermions are replaced by a dynamical scalar field whose nonzero expected value corresponds to the heavy fermion phase. This removes the fermion sign problem, making the effective action amenable to numerical studies as the effective theory is bosonic. Renormalization group studies of the effective action can be performed to extract approximations to nonperturbative effects at the transition. By performing one-loop renormalizations, resummed via Callan-Symanzik methods, we describe the heavy fermion criticality and predict the heavy fermion critical dynamical susceptibility and critical specific heat. The specific heat coefficient exponent we obtain (0.39) is in excellent agreement with the experimental result at low temperatures (0.4).Comment: 5 pages. In the replacement, the numerical value for the specific heat coefficient exponent has been included explicitly in decimal form, and has been compared with the experimental result

Influence of meditation on anti-correlated networks in the brain

Human experiences can be broadly divided into those that are external and related to interaction with the environment, and experiences that are internal and self-related. The cerebral cortex appears to be divided into two corresponding systems: an “extrinsic” system composed of brain areas that respond more to external stimuli and tasks and an “intrinsic” system composed of brain areas that respond less to external stimuli and tasks. These two broad brain systems seem to compete with each other, such that their activity levels over time is usually anti-correlated, even when subjects are “at rest” and not performing any task. This study used meditation as an experimental manipulation to test whether this competition (anti-correlation) can be modulated by cognitive strategy. Participants either fixated without meditation (fixation), or engaged in non-dual awareness (NDA) or focused attention (FA) meditations. We computed inter-area correlations (“functional connectivity”) between pairs of brain regions within each system, and between the entire extrinsic and intrinsic systems. Anti-correlation between extrinsic vs. intrinsic systems was stronger during FA meditation and weaker during NDA meditation in comparison to fixation (without mediation). However, correlation between areas within each system did not change across conditions. These results suggest that the anti-correlation found between extrinsic and intrinsic systems is not an immutable property of brain organization and that practicing different forms of meditation can modulate this gross functional organization in profoundly different ways

Анализ современных систем обнаружения утечек нефти на линейной части магистральных нефтепроводов

Фокус работы лежит на исследовании методов обнаружения утечек на линейной части магистрального нефтепровода, включающих параметрические методы, а также их внедрение. Кроме этого, рассматриваются системы обнаружения утечек, базирующиеся волоконно-оптическом кабеле и на аппаратуре, улавливающей акустическую эмиссию. Сравниваются системы на способность их адекватного реагирования на утечки. Отдельное внимание уделяется тестированию параметрических систем обнаружения утечек. На основе этого выполняется расчет на сокращение рисков в результате использования системThe focus of this work is on analysing leak detection methods applied on a oil pipeline, including those of measuring hydralics and their installation. Exept for this, leak detection systems are discribed, based on a fibre optic cable and equipment, that measures acoustic emission. Systems are compared in abbility to response to a leak. Ways of performing a testing is also taken into account. On this basis risk reduction colculation is carried out due to using leak detection systems

Adaptive whitening in neural populations with gain-modulating interneurons

Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems. Existing neural circuit models of adaptive whitening operate by modifying synaptic interactions; however, such modifications would seem both too slow and insufficiently reversible. Motivated by the extensive neuroscience literature on gain modulation, we propose an alternative model that adaptively whitens its responses by modulating the gains of individual neurons. Starting from a novel whitening objective, we derive an online algorithm that whitens its outputs by adjusting the marginal variances of an overcomplete set of projections. We map the algorithm onto a recurrent neural network with fixed synaptic weights and gain-modulating interneurons. We demonstrate numerically that sign-constraining the gains improves robustness of the network to ill-conditioned inputs, and a generalization of the circuit achieves a form of local whitening in convolutional populations, such as those found throughout the visual or auditory systems.Comment: 20 pages, 10 figures (incl. appendix). To appear in the Proceedings of the 40th International Conference on Machine Learnin