Autism, Movement, Time and Thought E-Motion Mis-Sight and other Temporo-Spatial Processing Disorders in Autism

In this chapter we propose a new approach of autism called E-Motion mis-sight and other temporospatial processing disorders. According to our view, subjects with autistic spectrum disorders (ASD) present more or less disabilities, delays and deviances, to perceive and integrate environmental world's sensory events online and to produce real-time sensorymotor coupling and adequate verbal and nonverbal outputs, from the beginning of their life. In other words, the environmental world is going and changing too fast for persons with ASD.In the first paragraph, we present some biographical self-reports, clinical considerations and neuropsychological arguments, which open windows on the peculiar visual and visuo-motor world of autistic persons. In the second paragraph, we expose the available experimental results in favour of physical and biological motion integration disorders in autistic population, and we expose a first synthesis of our approach. In the third paragraph, we review some results demonstrating other temporo-spatial processing disorders in ASD, and suggest some possible underlying neurobiological mechanisms of our E-Motion mis-sight and other temporospatial processing disorders hypothesis of autism, based on putative multi-system temporal dissynchronization and functional disconnectivity. We think that this approach, which is compatible with the major contemporary theories of ASD, may have new implications for the comprehension and new applications for the rehabilitation of these disorders. In the last paragraph, we propose some psychological and philosophical perspectives of our approach concerning with the integration of movement and time in thought, and the mind-brain relationships in autism in particular, and in human being in general

Hierarchical psychophysiological pathways subtend perceptual asymmetries in Neglect

Stroke patients with left Hemispatial Neglect (LHN) show deficits in perceiving left contralesional stimuli with biased visuospatial perception towards the right hemifield. However, very little is known about the functional organization of the visuospatial perceptual neural network and how this can account for the profound reorganization of space representation in LHN. In the present work, we aimed at (1) identifying EEG measures that discriminate LHN patients against controls and (2) devise a causative neurophysiological model between the discriminative EEG measures. To these aims, EEG was recorded during exposure to lateralized visual stimuli which allowed for pre-and post-stimulus activity investigation across three groups: LHN patients, lesioned controls, and healthy individuals. Moreover, all participants performed a standard behavioral test assessing the perceptual asymmetry index in detecting lateralized stimuli. The between-groups discriminative EEG patterns were entered into a Structural Equation Model for the identification of causative hierarchical associations (i.e., pathways) between EEG measures and the perceptual asymmetry index. The model identified two pathways. A first pathway showed that the combined contribution of pre-stimulus frontoparietal connectivity and individual-alpha-frequency predicts post-stimulus processing, as measured by visual-evoked N100, which, in turn, predicts the perceptual asymmetry index. A second pathway directly links the inter-hemispheric distribution of alpha-amplitude with the perceptual asymmetry index. The two pathways can collectively explain 83.1% of the variance in the perceptual asymmetry index. Using causative modeling, the present study identified how psychophysiological correlates of visuospatial perception are organized and predict the degree of behavioral asymmetry in LHN patients and controls

Event-Driven Technologies for Reactive Motion Planning: Neuromorphic Stereo Vision and Robot Path Planning and Their Application on Parallel Hardware

Die Robotik wird immer mehr zu einem Schlüsselfaktor des technischen Aufschwungs. Trotz beeindruckender Fortschritte in den letzten Jahrzehnten, übertreffen Gehirne von Säugetieren in den Bereichen Sehen und Bewegungsplanung noch immer selbst die leistungsfähigsten Maschinen. Industrieroboter sind sehr schnell und präzise, aber ihre Planungsalgorithmen sind in hochdynamischen Umgebungen, wie sie für die Mensch-Roboter-Kollaboration (MRK) erforderlich sind, nicht leistungsfähig genug. Ohne schnelle und adaptive Bewegungsplanung kann sichere MRK nicht garantiert werden. Neuromorphe Technologien, einschließlich visueller Sensoren und Hardware-Chips, arbeiten asynchron und verarbeiten so raum-zeitliche Informationen sehr effizient. Insbesondere ereignisbasierte visuelle Sensoren sind konventionellen, synchronen Kameras bei vielen Anwendungen bereits überlegen. Daher haben ereignisbasierte Methoden ein großes Potenzial, schnellere und energieeffizientere Algorithmen zur Bewegungssteuerung in der MRK zu ermöglichen. In dieser Arbeit wird ein Ansatz zur flexiblen reaktiven Bewegungssteuerung eines Roboterarms vorgestellt. Dabei wird die Exterozeption durch ereignisbasiertes Stereosehen erreicht und die Pfadplanung ist in einer neuronalen Repräsentation des Konfigurationsraums implementiert. Die Multiview-3D-Rekonstruktion wird durch eine qualitative Analyse in Simulation evaluiert und auf ein Stereo-System ereignisbasierter Kameras übertragen. Zur Evaluierung der reaktiven kollisionsfreien Online-Planung wird ein Demonstrator mit einem industriellen Roboter genutzt. Dieser wird auch für eine vergleichende Studie zu sample-basierten Planern verwendet. Ergänzt wird dies durch einen Benchmark von parallelen Hardwarelösungen wozu als Testszenario Bahnplanung in der Robotik gewählt wurde. Die Ergebnisse zeigen, dass die vorgeschlagenen neuronalen Lösungen einen effektiven Weg zur Realisierung einer Robotersteuerung für dynamische Szenarien darstellen. Diese Arbeit schafft eine Grundlage für neuronale Lösungen bei adaptiven Fertigungsprozesse, auch in Zusammenarbeit mit dem Menschen, ohne Einbußen bei Geschwindigkeit und Sicherheit. Damit ebnet sie den Weg für die Integration von dem Gehirn nachempfundener Hardware und Algorithmen in die Industrierobotik und MRK

Neural Coordination of Distinct Motor Learning Strategies: Latent Neurofunctional Mechanisms Elucidated via Computational Modeling

In this dissertation, a neurofunctional theory of learning is presented as an extension of functional analysis. This new theory clarifies the distinction— via applied quantitative analysis— between functionally intrinsic (essential) mechanistic structures and irrelevant structural details. This thesis is supported by a review of the relevant literature to provide historical context and sufficient scientific background. Further, the scope of this thesis is elucidated by two questions that are posed from a neurofunctional perspective— (1) how can specialized neuromorphology contribute to the functional dynamics of neural learning processes? (2) Can large-scale neurofunctional pathways emerge via inter-network communication between disparate neural circuits? These questions motivate the specific aims of this dissertation. Each aim is addressed by posing a relevant hypothesis, which is then tested via a neurocomputational experiment. In each experiment, computational techniques are leveraged to elucidate specific mechanisms that underlie neurofunctional learning processes. For instance, the role of specialized neuromorphology is investigated via the development of a computational model that replicates the neurophysiological mechanisms that underlie cholinergic interneurons’ regulation of dopamine in the striatum during reinforcement learning. Another research direction focuses on the emergence of large-scale neurofunctional pathways that connect the cerebellum and basal ganglia— this study also involves the construction of a neurocomputational model. The results of each study illustrate the capability of neurocomputational models to replicate functional learning dynamics of human subjects during a variety of motor adaptation tasks. Finally, the significance— and some potential applications— of neurofunctional theory are discussed

Facial feature representation and recognition

Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression representation and recognition have become a promising research area during recent years. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this dissertation, the fundamental techniques will be first reviewed, and the developments of the novel algorithms and theorems will be presented later. The objective of the proposed algorithm is to provide a reliable, fast, and integrated procedure to recognize either seven prototypical, emotion-specified expressions (e.g., happy, neutral, angry, disgust, fear, sad, and surprise in JAFFE database) or the action units in CohnKanade AU-coded facial expression image database. A new application area developed by the Infant COPE project is the recognition of neonatal facial expressions of pain (e.g., air puff, cry, friction, pain, and rest in Infant COPE database). It has been reported in medical literature that health care professionals have difficulty in distinguishing newborn\u27s facial expressions of pain from facial reactions of other stimuli. Since pain is a major indicator of medical problems and the quality of patient care depends on the quality of pain management, it is vital that the methods to be developed should accurately distinguish an infant\u27s signal of pain from a host of minor distress signal. The evaluation protocol used in the Infant COPE project considers two conditions: person-dependent and person-independent. The person-dependent means that some data of a subject are used for training and other data of the subject for testing. The person-independent means that the data of all subjects except one are used for training and this left-out one subject is used for testing. In this dissertation, both evaluation protocols are experimented. The Infant COPE research of neonatal pain classification is a first attempt at applying the state-of-the-art face recognition technologies to actual medical problems. The objective of Infant COPE project is to bypass these observational problems by developing a machine classification system to diagnose neonatal facial expressions of pain. Since assessment of pain by machine is based on pixel states, a machine classification system of pain will remain objective and will exploit the full spectrum of information available in a neonate\u27s facial expressions. Furthermore, it will be capable of monitoring neonate\u27s facial expressions when he/she is left unattended. Experimental results using the Infant COPE database and evaluation protocols indicate that the application of face classification techniques in pain assessment and management is a promising area of investigation. One of the challenging problems for building an automatic facial expression recognition system is how to automatically locate the principal facial parts since most existing algorithms capture the necessary face parts by cropping images manually. In this dissertation, two systems are developed to detect facial features, especially for eyes. The purpose is to develop a fast and reliable system to detect facial features automatically and correctly. By combining the proposed facial feature detection, the facial expression and neonatal pain recognition systems can be robust and efficient

2018 Symposium Brochure

This dissertation explores the mean field Heisenberg spin system and its evolution in time. We first study the system in equilibrium, where we explore the tool known as Stein's method, used for determining convergence rates to thermodynamic limits, both in an example proof for a mean field Ising system and in tightening a previous result for the equilibrium mean field Heisenberg system. We then model the evolution of the mean field Heisenberg model using Glauber dynamics and use this method to test the equilibrium results of two previous papers, uncovering a typographical error in one. Agreement in other aspects between theory and our simulations validates our approach in the equilibrium case. Next, we compare the evolution of the Heisenberg system under Glauber dynamics to a number of forms of Brownian motion and determine that Brownian motion is a poor match in most situations. Turning back to Stein's method, we consider what sort of proof regarding the behavior of the mean field Heisenberg model over time is obtainable and look at several possible routes to that path. We finish up by offering a Stein's method approach to understanding the evolution of the mean field Heisenberg model and offer some insight into its convergence in time to a thermodynamic limit. This demonstrates the potential usefulness of Stein's method in understanding the finite time behavior of evolving systems. In our efforts, we encounter several holes in current mathematical and physical knowledge. In particular, we suggest the development of tools for Markov chains currently unavailable and the development of a more physically based algorithm for the evolution of Heisenberg systems. These projects lie beyond the scope of this dissertation but it is our hope that these ideas may be useful to future research

Visual Cortex

The neurosciences have experienced tremendous and wonderful progress in many areas, and the spectrum encompassing the neurosciences is expansive. Suffice it to mention a few classical fields: electrophysiology, genetics, physics, computer sciences, and more recently, social and marketing neurosciences. Of course, this large growth resulted in the production of many books. Perhaps the visual system and the visual cortex were in the vanguard because most animals do not produce their own light and offer thus the invaluable advantage of allowing investigators to conduct experiments in full control of the stimulus. In addition, the fascinating evolution of scientific techniques, the immense productivity of recent research, and the ensuing literature make it virtually impossible to publish in a single volume all worthwhile work accomplished throughout the scientific world. The days when a single individual, as Diderot, could undertake the production of an encyclopedia are gone forever. Indeed most approaches to studying the nervous system are valid and neuroscientists produce an almost astronomical number of interesting data accompanied by extremely worthy hypotheses which in turn generate new ventures in search of brain functions. Yet, it is fully justified to make an encore and to publish a book dedicated to visual cortex and beyond. Many reasons validate a book assembling chapters written by active researchers. Each has the opportunity to bind together data and explore original ideas whose fate will not fall into the hands of uncompromising reviewers of traditional journals. This book focuses on the cerebral cortex with a large emphasis on vision. Yet it offers the reader diverse approaches employed to investigate the brain, for instance, computer simulation, cellular responses, or rivalry between various targets and goal directed actions. This volume thus covers a large spectrum of research even though it is impossible to include all topics in the extremely diverse field of neurosciences