Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.

Photoplethysmography (PPG) is a non-invasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research in the biomedical and clinical communities. Nevertheless, its single spot monitoring and the need to apply a PPG sensor directly to the skin limit its practicality in situations such as perfusion mapping and healing assessments or when free movement is required. The introduction of fast digital cameras into clinical imaging monitoring and diagnosis systems, the desire to reduce the physical restrictions, and the possible new insights that might come from perfusion imaging and mapping inspired the evolution of conventional PPG technology to imaging PPG (IPPG). IPPG is a noncontact method that can detect heart-generated pulse waves by means of peripheral blood perfusion measurements. Since its inception, IPPG has attracted significant public interest and provided opportunities to improve personal healthcare. This study presents an overview of the wide range of IPPG systems currently being introduced along with examples of their application in various physiological assessments. We believe that the widespread acceptance of IPPG is happening, and it will dramatically accelerate the promotion of this healthcare model in the near future.

Abstract-This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous Address Event Representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winnertake-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5%±3.5%) for a previously published four class card pip recognition task and an accuracy of 84.9%±1.9% for a new more difficult 36 class character recognition task.

Neural implants have emerged over the last decade as highly effective solutions for the treatment of dysfunctions and disorders of the nervous system. These implants establish a direct, often bidirectional, interface to the nervous system, both sensing neural signals and providing therapeutic treatments. As a result of the technological progress and successful clinical demonstrations, completely implantable solutions have become a reality and are now commercially available for the treatment of various functional disorders. Central to this development is the wireless power transfer (WPT) that has enabled implantable medical devices (IMDs) to function for extended durations in mobile subjects. In this review, we present the theory, link design, and challenges, along with their probable solutions for the traditional near-field resonant inductively coupled WPT, capacitively coupled short-ranged WPT, and more recently developed ultrasonic, mid-field, and far-field coupled WPT technologies for implantable applications. A comparison of various power transfer methods based on their power budgets and WPT range follows. Power requirements of specific implants like cochlear, retinal, cortical, and peripheral are also considered and currently available IMD solutions are discussed. Patient's safety concerns with respect to electrical, biological, physical, electromagnetic interference, and cyber security from an implanted neurotech device are also explored in this review. Finally, we discuss and anticipate future developments that will enhance the capabilities of current-day wirelessly powered implants and make them more efficient and integrable with other electronic components in IMDs.

Objective We used native sensorimotor representations of fingers in a brain-machine interface to achieve immediate online control of individual prosthetic fingers. Approach Using high gamma responses recorded with a high-density ECoG array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: 1) if any finger was moving, and, if so, 2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory (JHU/APL) Modular Prosthetic Limb (MPL). Main Results The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Significance Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time.

Laser Speckle Contrast Imaging (LSCI) is a wide field of view, non scanning optical technique for observing blood flow. Speckles are produced when coherent light scattered back from biological tissue is diffracted through the limiting aperture of focusing optics. Mobile scatterers cause the speckle pattern to blur; a model can be constructed by inversely relating the degree of blur, termed speckle contrast to the scatterer speed. In tissue, red blood cells are the main source of moving scatterers. Therefore, blood flow acts as a virtual contrast agent, outlining blood vessels. The spatial resolution (~10 μm) and temporal resolution (10 ms to 10 s) of LSCI can be tailored to the application. Restricted by the penetration depth of light, LSCI can only visualize superficial blood flow. Additionally, due to its non scanning nature, LSCI is unable to provide depth resolved images. The simple setup and non-dependence on exogenous contrast agents have made LSCI a popular tool for studying vascular structure and blood flow dynamics. We discuss the theory and practice of LSCI and critically analyze its merit in major areas of application such as retinal imaging, imaging of skin perfusion as well as imaging of neurophysiology.

Study of the human neurotrophic herpesvirus varicella-zoster virus (VZV) and of its ability to infect neurons has been severely limited by strict viral human tropism and limited availability of human neurons for experimentation. Human embryonic stem cells (hESC) can be differentiated to all the cell types of the body including neurons and are therefore a potentially unlimited source of human neurons to study their interactions with human neurotropic viruses. We report here reproducible infection of hESC-derived neurons by cell-associated green fluorescent protein (GFP)-expressing VZV. hESC-derived neurons expressed GFP within 2 days after incubation with mitotically inhibited MeWo cells infected with recombinant VZV expressing GFP as GFP fusions to VZV proteins or under an independent promoter. VZV infection was confirmed by immunostaining for immediate-early and viral capsid proteins. Infection of hESC-derived neurons was productive, resulting in release into the medium of infectious virions that appeared fully assembled when observed by electron microscopy. We also demonstrated, for the first time, VZV infection of axons and retrograde transport from axons to neuronal cell bodies using compartmented microfluidic chambers. The use of hESC-derived human neurons in conjunction with fluorescently tagged VZV shows great promise for the study of VZV neuronal infection and axonal transport and has potential for the establishment of a model for VZV latency in human neurons.The interactions of the human neurotrophic herpesvirus varicella-zoster virus (VZV) with neurons have proven difficult to study because the virus shows fairly strict human specificity, and small-animal models do not fully recapitulate human disease. In humans, primary VZV infection follows viral inhalation and subsequent systemic delivery to the deep dermis of the skin via hemopoietic cells. In the course of the resulting disease (chickenpox), VZV infects sensory and sympathetic ganglion neurons, where it establishes a long period of latency. The infection of neurons may take place in the ganglia by circulating VZV-infected lymphocytes, or by virus infecting cutaneous nerve endings being retrogradely transported in the axon to the neuronal somata, as is the case with herpes simplex virus (HSV). VZV reactivation often leads to herpes zoster (shingles), a disease that is frequently associated with severe, debilitating, and often long-lasting intractable pain (postherpetic neuralgia) that is more often than not refractory to therapy.Few model systems of neuronal VZV infection have been developed. Two in vitro models are VZV infection of dissociated human neurons and intact human fetal dorsal root ganglia (DRG) (8, 9, 10). These studies have shed some light on VZV-neuronal interactions, demonstrating, for example, that VZV exerts antiapoptotic activities in neurons in the short term (maximum, 5 days) and that, unlike infected fibroblasts, infectious VZV is released from neurons.A human fetal DRG-SCID mouse model (22, 29; reviewed in reference 30) has al...

Optogenetics is an optical technique that exploits visible light for selective neuromodulation with spatio-temporal precision. Despite enormous effort, the effective stimulation of targeted neurons, which are located in deeper structures of the nervous system, by visible light, remains a technical challenge. Compared to visible light, near-infrared illumination offers a higher depth of tissue penetration owing to a lower degree of light attenuation. Herein, an overview of advances in developing new modalities for neural circuitry modulation utilizing upconversion-nanoparticlemediated optogenetics is presented. These developments have led to minimally invasive optical stimulation and inhibition of neurons with substantially improved selectivity, sensitivity, and spatial resolution. The focus is to provide a comprehensive review of the mechanistic basis for evaluating upconversion parameters, which will be useful in designing, executing, and reporting optogenetic experiments.