Efficient Universal Computing Architectures for Decoding Neural Activity

The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain– machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain– machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than . We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA) implementation of this portion is consequently energy efficient. We validate the performance of our overall system by decoding electrophysiologic data from a behaving rodent.United States. National Institutes of Health (Grant NS056140

Surgical techniques for evacuation of chronic subdural hematoma: a mini-review

Chronic subdural hematoma is one of the most common neurosurgical pathologies with over 160,000 cases in the United States and Europe each year. The current standard of care involves surgically evacuating the hematoma through a cranial opening, however, varied patient risk profiles, a significant recurrence rate, and increasing financial burden have sparked innovation in the field. This mini-review provides a brief overview of currently used evacuation techniques, including emerging adjuncts such as endoscopic assistance and middle meningeal artery embolization. This review synthesizes the body of available evidence on efficacy and risk profiles for each critical aspect of surgical technique in cSDH evacuation and provides insight into trends in the field and promising new technologies

Low-Power Circuits for Brain–Machine Interfaces

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode- recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented

A prospective clinical trial on the influence of a triamcinolone/demeclocycline and a calcium hydroxide based temporary cement on pain perception

<p>Abstract</p> <p>Introduction</p> <p>The aim of this clinical trial was to compare the degree of short term post-operative irritation after application of a triamcinolone/demeclocycyline based or a calcium hydroxide based provisional cement.</p> <p>Methods</p> <p>A total of 109 patients (55 female and 54 male; mean age: 51 ± 14 years) with primary or secondary dentinal caries were randomly assigned to the two treatment groups of this biomedical clinical trial (phase III). Selection criteria were good systemic health and treated teeth, which were vital and showed no symptoms of pulpitis. Up to three teeth were prepared for indirect metallic restorations, and the provisional restorations were cemented with a triamcinolone/demeclocycyline (Ledermix) or a calcium hydroxide (Provicol) based material. The intensity of post-operative pain experienced was documented according to the VAS (4, 12, 20, 24, and 82 h) and compared to VAS baseline.</p> <p>Results</p> <p>A total of 159 teeth were treated (Ledermix: 83 teeth, Provicol: 76 teeth). The minor irritation of the teeth, experienced prior to treatment, was similar in both groups; however, 4 h after treatment this value was significantly higher in the Provicol group than in the Ledermix group (p < 0.005, t-test). After 12 h, the difference was no longer significant. The number of patients taking analgesics for post-treatment pain was higher in the Provicol group (n = 11/53) than in the Ledermix group (n = 3/56).</p> <p>Conclusions</p> <p>The patients had no long term post-operative pain experience in both groups. However, within the first hours after cementation the sensation of pain was considerably higher in the Provicol group than in the Ledermix group.</p

A Glucose Fuel Cell for Implantable Brain–Machine Interfaces

We have developed an implantable fuel cell that generates power through glucose oxidation, producing steady-state power and up to peak power. The fuel cell is manufactured using a novel approach, employing semiconductor fabrication techniques, and is therefore well suited for manufacture together with integrated circuits on a single silicon wafer. Thus, it can help enable implantable microelectronic systems with long-lifetime power sources that harvest energy from their surrounds. The fuel reactions are mediated by robust, solid state catalysts. Glucose is oxidized at the nanostructured surface of an activated platinum anode. Oxygen is reduced to water at the surface of a self-assembled network of single-walled carbon nanotubes, embedded in a Nafion film that forms the cathode and is exposed to the biological environment. The catalytic electrodes are separated by a Nafion membrane. The availability of fuel cell reactants, oxygen and glucose, only as a mixture in the physiologic environment, has traditionally posed a design challenge: Net current production requires oxidation and reduction to occur separately and selectively at the anode and cathode, respectively, to prevent electrochemical short circuits. Our fuel cell is configured in a half-open geometry that shields the anode while exposing the cathode, resulting in an oxygen gradient that strongly favors oxygen reduction at the cathode. Glucose reaches the shielded anode by diffusing through the nanotube mesh, which does not catalyze glucose oxidation, and the Nafion layers, which are permeable to small neutral and cationic species. We demonstrate computationally that the natural recirculation of cerebrospinal fluid around the human brain theoretically permits glucose energy harvesting at a rate on the order of at least 1 mW with no adverse physiologic effects. Low-power brain–machine interfaces can thus potentially benefit from having their implanted units powered or recharged by glucose fuel cells

Metabolic Factors Limiting Performance in Marathon Runners

Each year in the past three decades has seen hundreds of thousands of runners register to run a major marathon. Of those who attempt to race over the marathon distance of 26 miles and 385 yards (42.195 kilometers), more than two-fifths experience severe and performance-limiting depletion of physiologic carbohydrate reserves (a phenomenon known as ‘hitting the wall’), and thousands drop out before reaching the finish lines (approximately 1–2% of those who start). Analyses of endurance physiology have often either used coarse approximations to suggest that human glycogen reserves are insufficient to fuel a marathon (making ‘hitting the wall’ seem inevitable), or implied that maximal glycogen loading is required in order to complete a marathon without ‘hitting the wall.’ The present computational study demonstrates that the energetic constraints on endurance runners are more subtle, and depend on several physiologic variables including the muscle mass distribution, liver and muscle glycogen densities, and running speed (exercise intensity as a fraction of aerobic capacity) of individual runners, in personalized but nevertheless quantifiable and predictable ways. The analytic approach presented here is used to estimate the distance at which runners will exhaust their glycogen stores as a function of running intensity. In so doing it also provides a basis for guidelines ensuring the safety and optimizing the performance of endurance runners, both by setting personally appropriate paces and by prescribing midrace fueling requirements for avoiding ‘the wall.’ The present analysis also sheds physiologically principled light on important standards in marathon running that until now have remained empirically defined: The qualifying times for the Boston Marathon

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change

Estimating the aerobic capacity of a typical runner.

<p>Computed approximations of (in terms of milliliters of oxygen per minute per kilogram body mass), as a function of estimated fraction of maximum heart rate while running at a given speed, are shown as a set of colored curves. Each line corresponds to a particular running speed (Orange, 4 mph (); Light Green, 5 mph (); Green, 6 mph (); Dark Green, 7 mph (); Light Blue, 8 mph (); Blue, 9 mph (); Dark Blue, 10 mph (); Purple, 11 mph (); Magenta, 12 mph (); Red, 13 mph ()). See the text for a detailed explanation.</p