The problem considered here is that of wireless information and power transfer across a noisy coupled-inductor circuit, which is a frequency-selective channel with additive white Gaussian noise. The optimal tradeoff between the achievable rate and the power transferred is characterized given the total power available. The practical utility of such systems is also discussed.

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Shannon meets Tesla: Wireless information and power transfer

Recent work in field of neuroprosthetics has demonstrated that by observing the simultaneous activity of many neurons in specific regions of the brain, it is possible to produce control signals that allow animals or humans to drive cursors or prosthetic limbs directly through thoughts. As neuroprosthetic devices transition from experimental to clinical use, there is a need for fully-implantable amplification and telemetry electronics in close proximity to the recording sites. To address these needs, we developed a prototype integrated circuit for wireless neural recording from a 100-channel microelectrode array. The design of both the system-level architecture and the individual circuits were driven by severe power constraints for small implantable devices; chronically heating tissue by only a few degrees Celsius leads to cell death. Due to the high data rate produced by 100 neural signals, the system must perform data reduction as well. We use a combination of a low-power ADC and an array of "spike detectors" to reduce the transmitted data rate while preserving critical information. The complete system receives power and commands (at 6.5 kb/s) wirelessly over a 2.64-MHz inductive link and transmits neural data back at a data rate of 330 kb/s using a fully-integrated 433-MHz FSK transmitter. The 4.7times5.9 mm2 chip was fabricated in a 0.5-mum 3M2P CMOS process and consumes 13.5 mW of power. While cross-chip interference limits performance in single-chip operation, a two-chip system was used to record neural signals from a Utah Electrode Array in cat cortex and transmit the digitized signals wirelessly to a receiver

/pdf/a-low-power-integrated-circuit-for-a-wireless-100-electrode-33b774psdo.pdf

A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System

Driven by the confluence between the need to collect data about people's physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to urban and the recent availability of the technologies that enable this data collection, wireless sensor networks for healthcare have emerged in the recent years. In this review, we present some representative applications in the healthcare domain and describe the challenges they introduce to wireless sensor networks due to the required level of trustworthiness and the need to ensure the privacy and security of medical data. These challenges are exacerbated by the resource scarcity that is inherent with wireless sensor network platforms. We outline prototype systems spanning application domains from physiological and activity monitoring to large-scale physiological and behavioral studies and emphasize ongoing research challenges.

/pdf/wireless-sensor-networks-for-healthcare-f8ee00rnzw.pdf

Wireless Sensor Networks for Healthcare

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

The ability to implant electronic systems in the human body has led to many medical advances. Progress in semiconductor technology paved the way for devices at the scale of a millimeter or less (“microimplants”), but the miniaturization of the power source remains challenging. Although wireless powering has been demonstrated, energy transfer beyond superficial depths in tissue has so far been limited by large coils (at least a centimeter in diameter) unsuitable for a microimplant. Here, we show that this limitation can be overcome by a method, termed midfield powering, to create a high-energy density region deep in tissue inside of which the power-harvesting structure can be made extremely small. Unlike conventional near-field (inductively coupled) coils, for which coupling is limited by exponential field decay, a patterned metal plate is used to induce spatially confined and adaptive energy transport through propagating modes in tissue. We use this method to power a microimplant (2 mm, 70 mg) capable of closed-chest wireless control of the heart that is orders of magnitude smaller than conventional pacemakers. With exposure levels below human safety thresholds, milliwatt levels of power can be transferred to a deep-tissue (>5 cm) microimplant for both complex electronic function and physiological stimulation. The approach developed here should enable new generations of implantable systems that can be integrated into the body at minimal cost and risk.

https://www.pnas.org/content/pnas/111/22/7974.full.pdf

Wireless power transfer to deep-tissue microimplants

RF wireless interface enables remotely-powered implantable devices. Current studies in wireless power transmission into biological tissue tend to operate below 10 MHz due to tissue absorption loss, which results in large receive antennas. This paper examines the range of frequencies that will optimize the tradeoff between received power and tissue absorption. It first models biological tissue as a dispersive dielectric in a homogeneous medium and performs full-wave analysis to show that the optimal frequency is above 1 GHz for small receive coil and typical transmit-receive separations. Then, it includes the air-tissue interface and models human body as a planarly layered medium. The optimal frequency is shown to remain in the GHz-range. Finally, electromagnetic simulations are performed to include the effect of load impedance and look at the matched power gain. The optimal frequency is in the GHz-range for mm-sized transmit antenna and shifts to the sub-GHz range for cm-sized transmit antenna. The multiple orders of magnitude increase in the operating frequency enables dramatic miniaturization of implantable devices.

/pdf/optimal-frequency-for-wireless-power-transmission-into-3rk2n77jvd.pdf

Optimal Frequency for Wireless Power Transmission Into Dispersive Tissue

Wireless powering of implanted devices obviates the need for batteries, which must be periodically replaced and constitute a health risk. There has long been an assumption that efficient wireless transfer of power to biomedical implants requires fields operating in the low MHz in order to avoid excess losses in tissue, thus requiring antenna diameters of a few cm [1]. Such antennae increase device size and thereby restrict the range of viable applications. This work presents a wireless power transfer system that uses an antenna area 100× smaller than previous designs [2] while maintaining the same power transfer efficiency over the same range.

A mm-sized implantable power receiver with adaptive link compensation

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that state-of-the-art spike sorting algorithms are not only feasible using modern complementary metal oxide semiconductor very large scale integration processes, but may represent the best option for extracting large amounts of data in implantable neural prosthetic interfaces.

/pdf/power-feasibility-of-implantable-digital-spike-sorting-19p4y5jmxo.pdf

Power feasibility of implantable digital spike-sorting circuits for neural prosthetic systems

Cortically controlled prostheses are able to translate neural activity from the cerebral cortex into control signals for guiding computer cursors or prosthetic limbs. While both noninvasive and invasive electrode techniques can be used to measure neural activity, the latter promises considerably higher levels of performance and therefore functionality to patients. The process of translating analog voltages recorded at the electrode tip into control signals for the prosthesis requires sophisticated signal acquisition and processing techniques. In this article we briefly review the current state-of-the-art in invasive, electrode-based neural prosthetic systems, with particular attention to the advanced signal processing algorithms that enable that performance. Improving prosthetic performance is only part of the challenge, however. A clinically viable prosthetic system will need to be more robust and autonomous and, unlike existing approaches that depend on multiple computers and specialized recording units, must be implemented in a compact, implantable prosthetic processor (IPP). In this article we summarize recent results which indicate that state-of-the-art prosthetic systems can be implemented in an IPP using current semiconductor technology, and the challenges that face signal processing engineers in improving prosthetic performance, autonomy and robustness within the restrictive constraints of the IPP.

/pdf/signal-processing-challenges-for-neural-prostheses-5b9fhqhka7.pdf

Signal Processing Challenges for Neural Prostheses

This paper examines short-range wireless powering for implantable devices and shows that existing analysis techniques are not adequate to conclude the characteristics of power transfer efficiency over a wide frequency range. It shows, theoretically and experimentally, that the optimal frequency for power transmission in biological media can be in the GHz-range while existing solutions exclusively focus on the MHz-range. This implies that the size of the receive coil can be reduced by 104 times which enables the realization of fully integrated implantable devices.

/pdf/optimal-operating-frequency-in-wireless-power-transmission-4ypwpznjbk.pdf

Stephen O'Driscoll

Papers

Optimal Frequency for Wireless Power Transmission Into Dispersive Tissue

A mm-sized implantable power receiver with adaptive link compensation

Power feasibility of implantable digital spike-sorting circuits for neural prosthetic systems

Signal Processing Challenges for Neural Prostheses

Optimal Operating Frequency in Wireless Power Transmission for Implantable Devices