Search

Vision restoration is an ideal medical application for optogenetics, because the eye provides direct optical access to the retina for stimulation. Optogenetic therapy could be used for diseases involving photoreceptor degeneration, such as retinitis pigmentosa or age-related macular degeneration. We describe here the selection, in non-human primates, of a specific optogenetic construct currently tested in a clinical trial. We used the microbial opsin ChrimsonR, and showed that the AAV2.7m8 vector had a higher transfection efficiency than AAV2 in retinal ganglion cells (RGCs) and that ChrimsonR fused to tdTomato (ChR-tdT) was expressed more efficiently than ChrimsonR. Light at 600 nm activated RGCs transfected with AAV2.7m8 ChR-tdT, from an irradiance of 10 15 photons.cm -2 .s -1 . Vector doses of 5 × 10 10 and 5 × 10 11 vg/eye transfected up to 7000 RGCs/mm 2 in the perifovea, with no significant immune reaction. We recorded RGC responses from a stimulus duration of 1 ms upwards. When using the recorded activity to decode stimulus information, we obtained an estimated visual acuity of 20/249, above the level of legal blindness (20/400). These results lay the groundwork for the ongoing clinical trial with the AAV2.7m8 - ChR-tdT vector for vision restoration in patients with retinitis pigmentosa.

Retinal dystrophies and age-related macular degeneration related to photoreceptor degeneration can cause blindness. In blind patients, although the electrical activation of the residual retinal circuit can provide useful artificial visual perception, the resolutions of current retinal prostheses have been limited either by large electrodes or small numbers of pixels. Here we report the evaluation, in three awake non-human primates, of a previously reported near-infrared-light-sensitive photovoltaic subretinal prosthesis. We show that multipixel stimulation of the prosthesis within radiation safety limits enabled eye tracking in the animals, that they responded to stimulations directed at the implant with repeated saccades and that the implant-induced responses were present two years after device implantation. Our findings pave the way for the clinical evaluation of the prosthesis in patients affected by dry atrophic age-related macular degeneration.

Depth from defocus is an important mechanism that enables vision systems to perceive depth. While machine vision has developed several algorithms to estimate depth from the amount of defocus present at the focal plane, existing techniques are slow, energy demanding and mainly relying on numerous acquisitions and massive amounts of filtering operations on the pixels' absolute luminance value. Recent advances in neuromorphic engineering allow an alternative to this problem, with the use of event-based silicon retinas and neural processing devices inspired by the organizing principles of the brain. In this paper, we present a low power, compact and computationally inexpensive setup to estimate depth in a 3D scene in real time at high rates that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. Exploiting the high temporal resolution of the event-based silicon retina, we are able to extract depth at 100 Hz for a power budget lower than a 200 mW (10 mW for the camera, 90 mW for the liquid lens and ~100 mW for the computation). We validate the model with experimental results, highlighting features that are consistent with both computational neuroscience and recent findings in the retina physiology. We demonstrate its efficiency with a prototype of a neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological depth from defocus experiments reported in the literature.

This letter presents a novel computationally efficient and robust pattern tracking method based on a time-encoded, frame-free visual data. Recent interdisciplinary developments, combining inputs from engineering and biology, have yielded a novel type of camera that encodes visual information into a continuous stream of asynchronous, temporal events. These events encode temporal contrast and intensity locally in space and time. We show that the sparse yet accurately timed information is well suited as a computational input for object tracking. In this letter, visual data processing is performed for each incoming event at the time it arrives. The method provides a continuous and iterative estimation of the geometric transformation between the model and the events representing the tracked object. It can handle isometry, similarities, and affine distortions and allows for unprecedented real-time performance at equivalent frame rates in the kilohertz range on a standard PC. Furthermore, by using the dimension of time that is currently underexploited by most artificial vision systems, the method we present is able to solve ambiguous cases of object occlusions that classical frame-based techniques handle poorly.

This paper presents a novel N-ocular 3D reconstruction algorithm for event-based vision data from bio-inspired artificial retina sensors. Artificial retinas capture visual information asynchronously and encode it into streams of asynchronous spike-like pulse signals carrying information on, e.g., temporal contrast events in the scene. The precise time of the occurrence of these visual features are implicitly encoded in the spike timings. Due to the high temporal resolution of the asynchronous visual information acquisition, the output of these sensors is ideally suited for dynamic 3D reconstruction. The presented technique takes full benefit of the event-driven operation, i.e. events are processed individually at the moment they arrive. This strategy allows us to preserve the original dynamics of the scene, hence allowing for more robust 3D reconstructions. As opposed to existing techniques, this algorithm is based on geometric and time constraints alone, making it particularly simple to implement and largely linear., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

This paper introduces a process to compute optical flow using an asynchronous event-based retina at high speed and low computational load. A new generation of artificial vision sensors has now started to rely on biologically inspired designs for light acquisition. Biological retinas, and their artificial counterparts, are totally asynchronous and data driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework for processing visual data using asynchronous event-based acquisition, providing a method for the evaluation of optical flow. The paper shows that current limitations of optical flow computation can be overcome by using event-based visual acquisition, where high data sparseness and high temporal resolution permit the computation of optical flow with micro-second accuracy and at very low computational cost., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

In this study, a bio-inspired approach for extracting efficient features prior to the recognition of scenes is proposed. It is highly inspired from the model of the mammals visual system. The retina contains many levels of neurons (bipolar, amacrine, horizontal and ganglion cells) accurately organized from cones and rods to the optic nerve up till the lateral geniculate nucleus (LGN) which is the main thalamic relay for inputs to the visual cortex. This structure probably eases other brain areas tasks in preprocessing the visual information. This paper is focusing on the study of these specific structures, relying on a bottom up approach to propose a comprehensive mathematical model of the low level image processing performed within the eye. The presented system takes into account the foveolar structure of the retina to produce a low-resolution representation of observed images by decomposing them into a local summation of elementary gaussian color histograms. This representation corresponds to the LGN biological organization. It has been thought that due to short timings, some very quick localization tasks involving particularly fast information processing pathways cannot be provided by the classical ones passing through higher level cortical areas. This work proposes a model of retinal coding and LGN-visual representation that we show provides reliable and sufficient early features for scenes recognition and localization. Experiments on real scenes using the developed model are presented showing the efficiency of the approach on localization.