A novel approach to robot vision using a hexagonal grid and spiking neural networks

Many robots use range data to obtain an almost 3-dimensional description of their environment. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Inspired by the structure and behaviour of the human visual system, we present an approach to feature extraction in range data using spiking neural networks and a biologically plausible hexagonal pixel arrangement. Standard digital images are converted into a hexagonal pixel representation and then processed using a spiking neural network with hexagonal shaped receptive fields; this approach is a step towards developing a robotic eye that closely mimics the human eye. The performance is compared with receptive fields implemented on standard rectangular images. Results illustrate that, using hexagonally shaped receptive fields, performance is improved over standard rectangular shaped receptive fields

TOWARDS A COMPUTATIONAL MODEL OF RETINAL STRUCTURE AND BEHAVIOR

Human vision is our most important sensory system, allowing us to perceive our surroundings. It is an extremely complex process that starts with light entering the eye and ends inside of the brain, with most of its mechanisms still to be explained. When we observe a scene, the optics of the eye focus an image on the retina, where light signals are processed and sent all the way to the visual cortex of the brain, enabling our visual sensation. The progress of retinal research, especially on the topography of photoreceptors, is often tied to the progress of retinal imaging systems. The latest adaptive optics techniques have been essential for the study of the photoreceptors and their spatial characteristics, leading to discoveries that challenge the existing theories on color sensation. The organization of the retina is associated with various perceptive phenomena, some of them are straightforward and strictly related to visual performance like visual acuity or contrast sensitivity, but some of them are more difficult to analyze and test and can be related to the submosaics of the three classes of cone photoreceptors, like how the huge interpersonal differences between the ratio of different cone classes result in negligible differences in color sensation, suggesting the presence of compensation mechanisms in some stage of the visual system. In this dissertation will be discussed and addressed issues regarding the spatial organization of the photoreceptors in the human retina. A computational model has been developed, organized into a modular pipeline of extensible methods each simulating a different stage of visual processing. It does so by creating a model of spatial distribution of cones inside of a retina, then applying descriptive statistics for each photoreceptor to contribute to the creation of a graphical representation, based on a behavioral model that determines the absorption of photoreceptors. These apparent color stimuli are reconstructed in a representation of the observed scene. The model allows the testing of different parameters regulating the photoreceptor's topography, in order to formulate hypothesis on the perceptual differences arising from variations in spatial organization

Developing a new generation of neuro-prosthetic interfaces: structure-function correlates of viable retina-CNT biohybrids

PhD ThesisOne of the many challenges in the development of neural prosthetic devices is the choice of electrode material. Electrodes must be biocompatible, and at the same time, they must be able to sustain repetitive current injections in a highly corrosive physiological environment. We investigated the suitability of carbon nanotube (CNT) electrodes for retinal prosthetics by studying prolonged exposure to retinal tissue and repetitive electrical stimulation of retinal ganglion cells (RGCs). Experiments were performed on retinal wholemounts isolated from the Cone rod homeobox (CRX) knockout mouse, a model of Leber congenital amaurosis. Retinas were interfaced at the vitreo-retinal juncture with CNT assemblies and maintained in physiological conditions for up to three days to investigate any anatomical (immunohistochemistry and electron microscopy) and electrophysiological changes (multielectrode array stimulation and recordings; electrodes were made of CNTs or commercial titanium nitride). Anatomical characterisation of the inner retina, including RGCs, astrocytes and Müller cells as well as cellular matrix and inner retinal vasculature, provide strong evidence of a gradual remodelling of the retina to incorporate CNT assemblies, with very little indication of an immune response. Prolonged electrophysiological recordings, performed over the course of three days, demonstrate a gradual increase in signal amplitudes, lowering of stimulation thresholds and an increase in cellular recruitment for RGCs interfaced with CNT electrodes, but not with titanium nitride electrodes. These results provide for the first time electrophysiological, ultrastructural and cellular evidence of the time-dependent formation of strong and viable bio-hybrids between the RGC layer and CNT arrays in intact retinas. We conclude that CNTs are a promising material for inclusion in retinal prosthetic devices

An Optoelectronic Stimulator for Retinal Prosthesis

Retinal prostheses require the presence of viable population of cells in the inner retina. Evaluations of retina with Age-Related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP) have shown a large number of cells remain in the inner retina compared with the outer retina. Therefore, vision loss caused by AMD and RP is potentially treatable with retinal prostheses. Photostimulation based retinal prostheses have shown many advantages compared with retinal implants. In contrary to electrode based stimulation, light does not require mechanical contact. Therefore, the system can be completely external and not does have the power and degradation problems of implanted devices. In addition, the stimulating point is flexible and does not require a prior decision on the stimulation location. Furthermore, a beam of light can be projected on tissue with both temporal and spatial precision. This thesis aims at fi nding a feasible solution to such a system. Firstly, a prototype of an optoelectronic stimulator was proposed and implemented by using the Xilinx Virtex-4 FPGA evaluation board. The platform was used to demonstrate the possibility of photostimulation of the photosensitized neurons. Meanwhile, with the aim of developing a portable retinal prosthesis, a system on chip (SoC) architecture was proposed and a wide tuning range sinusoidal voltage-controlled oscillator (VCO) which is the pivotal component of the system was designed. The VCO is based on a new designed Complementary Metal Oxide Semiconductor (CMOS) Operational Transconductance Ampli er (OTA) which achieves a good linearity over a wide tuning range. Both the OTA and the VCO were fabricated in the AMS 0.35 µm CMOS process. Finally a 9X9 CMOS image sensor with spiking pixels was designed. Each pixel acts as an independent oscillator whose frequency is controlled by the incident light intensity. The sensor was fabricated in the AMS 0.35 µm CMOS Opto Process. Experimental validation and measured results are provided

Egocentric Computer Vision and Machine Learning for Simulated Prosthetic Vision

Las prótesis visuales actuales son capaces de proporcionar percepción visual a personas con cierta ceguera. Sin pasar por la parte dañada del camino visual, la estimulación eléctrica en la retina o en el sistema nervioso provoca percepciones puntuales conocidas como “fosfenos”. Debido a limitaciones fisiológicas y tecnológicas, la información que reciben los pacientes tiene una resolución muy baja y un campo de visión y rango dinámico reducido afectando seriamente la capacidad de la persona para reconocer y navegar en entornos desconocidos. En este contexto, la inclusión de nuevas técnicas de visión por computador es un tema clave activo y abierto. En esta tesis nos centramos especialmente en el problema de desarrollar técnicas para potenciar la información visual que recibe el paciente implantado y proponemos diferentes sistemas de visión protésica simulada para la experimentación.Primero, hemos combinado la salida de dos redes neuronales convolucionales para detectar bordes informativos estructurales y siluetas de objetos. Demostramos cómo se pueden reconocer rápidamente diferentes escenas y objetos incluso en las condiciones restringidas de la visión protésica. Nuestro método es muy adecuado para la comprensión de escenas de interiores comparado con los métodos tradicionales de procesamiento de imágenes utilizados en prótesis visuales.Segundo, presentamos un nuevo sistema de realidad virtual para entornos de visión protésica simulada más realistas usando escenas panorámicas, lo que nos permite estudiar sistemáticamente el rendimiento de la búsqueda y reconocimiento de objetos. Las escenas panorámicas permiten que los sujetos se sientan inmersos en la escena al percibir la escena completa (360 grados).En la tercera contribución demostramos cómo un sistema de navegación de realidad aumentada para visión protésica ayuda al rendimiento de la navegación al reducir el tiempo y la distancia para alcanzar los objetivos, incluso reduciendo significativamente el número de colisiones de obstáculos. Mediante el uso de un algoritmo de planificación de ruta, el sistema encamina al sujeto a través de una ruta más corta y sin obstáculos. Este trabajo está actualmente bajo revisión.En la cuarta contribución, evaluamos la agudeza visual midiendo la influencia del campo de visión con respecto a la resolución espacial en prótesis visuales a través de una pantalla montada en la cabeza. Para ello, usamos la visión protésica simulada en un entorno de realidad virtual para simular la experiencia de la vida real al usar una prótesis de retina. Este trabajo está actualmente bajo revisión.Finalmente, proponemos un modelo de Spiking Neural Network (SNN) que se basa en mecanismos biológicamente plausibles y utiliza un esquema de aprendizaje no supervisado para obtener mejores algoritmos computacionales y mejorar el rendimiento de las prótesis visuales actuales. El modelo SNN propuesto puede hacer uso de la señal de muestreo descendente de la unidad de procesamiento de información de las prótesis retinianas sin pasar por el análisis de imágenes retinianas, proporcionando información útil a los ciegos. Esté trabajo está actualmente en preparación.<br /

Contour Integration in Artifical Neural Networks

Under difficult viewing conditions, the brain's visual system uses a variety of modulatory techniques to supplement its core feedforward signal. One such technique is contour integration, whereby contextual stimuli from outside the classically defined receptive fields of neurons can affect their responses. It manifests in the primary visual (V1) cortex, a low layer of the visual cortex, and can selectively enhance smooth contours. Several mechanistic models, that can account for many of its neurophysiological properties, have been proposed in the literature. However, there has been limited exploration of the learning of biologically realistic contour integration circuits or of the role of contour integration in the processing of natural images. In this thesis, I present a biologically-inspired model of contour integration embedded in a task-driven artificial neural network. The model can relate the low-level neural phenomenon of contour integration to the high-level goals of its encompassing system. It uses intra-area lateral connections and an internal architecture inspired by the V1 cortex. Its parameters are learnt from optimizing performance on high-level tasks rather than being fixed at initialization. When trained to detect contours in a background of random edges, a task commonly used to examine contour integration in the brain, the model learns to integrate contours in a manner consistent with the brain. This is validated by comparing the model with observed data at the behavioral, neurophysiological and neuroanatomical levels. The model is also used to explore the role of contour integration in the perception of natural scenes. I investigate which natural image tasks benefit from contour integration, how it affects their performances and the consistency of trained models with properties of contour integration from more extensively studied artificial stimuli. Specifically, the model was trained on two natural image tasks: detection of all edges and the ability to distinguish if two points lie on the same or different contours. In natural images, the model was found to enhance weaker contours and demonstrated many properties that were similar to when it was trained on synthetic stimuli. Moreover, the features it learnt were robust and generalized well to test data from outside the distribution of training data. The results provide new evidence that contour integration can improve visual perception and complex scene understanding

Bio-inspired electronics for micropower vision processing

Vision processing is a topic traditionally associated with neurobiology; known to encode, process and interpret visual data most effectively. For example, the human retina; an exquisite sheet of neurobiological wetware, is amongst the most powerful and efficient vision processors known to mankind. With improving integrated technologies, this has generated considerable research interest in the microelectronics community in a quest to develop effective, efficient and robust vision processing hardware with real-time capability. This thesis describes the design of a novel biologically-inspired hybrid analogue/digital vision chip ORASIS1 for centroiding, sizing and counting of enclosed objects. This chip is the first two-dimensional silicon retina capable of centroiding and sizing multiple objects2 in true parallel fashion. Based on a novel distributed architecture, this system achieves ultra-fast and ultra-low power operation in comparison to conventional techniques. Although specifically applied to centroid detection, the generalised architecture in fact presents a new biologically-inspired processing paradigm entitled: distributed asynchronous mixed-signal logic processing. This is applicable to vision and sensory processing applications in general that require processing of large numbers of parallel inputs, normally presenting a computational bottleneck. Apart from the distributed architecture, the specific centroiding algorithm and vision chip other original contributions include: an ultra-low power tunable edge-detection circuit, an adjustable threshold local/global smoothing network and an ON/OFF-adaptive spiking photoreceptor circuit. Finally, a concise yet comprehensive overview of photodiode design methodology is provided for standard CMOS technologies. This aims to form a basic reference from an engineering perspective, bridging together theory with measured results. Furthermore, an approximate photodiode expression is presented, aiming to provide vision chip designers with a basic tool for pre-fabrication calculations