This article presents a 915-MHz ultra-low-power (ULP) sub-sampling phase-tracking receiver (SSPT-RX). It is targeted for the power-constrained devices that need short range but medium high-speed data receiving, such as the multi-channel neural stimulator with arbitrary waveform generation. The zero-intermediate frequency (zero-IF) phase-tracking receiver (PT-RX) topology is adopted to simplify the sub-sampling RX architecture with direct demodulation of frequency-shift keying (FSK) signal while improving the image frequency issue. The frequency of local oscillator (LO) is reduced by ten times with the proposed architecture, which leads to greatly reduced power consumption. Fabricated in 65-nm CMOS process, the RX chip occupies an active area of 0.58 mm 2 . The RX consumes one of the lowest power consumptions of 164 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> from 0.5-/1-V supplies. It achieves an energy efficiency of 32.8 pJ/bit at a data rate of 5 Mb/s, which is improved by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 5.6\times $ </tex-math></inline-formula> compared to the state-of-the-art RXs. The measured sensitivity of 915-MHz FSK signal receiving is −69.5 dBm with an LO frequency of 91.5 MHz, which is 1/10 of the carrier frequency. The achieved RX sensitivity figure-of-merit (FoM) is 174.3 dB.

A 164-$\mu$ W 915-MHz Sub-Sampling Phase-Tracking Zero-IF Receiver With 5-Mb/s Data Rate for Short-Range Applications

Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear responses to electrical stimulation make it challenging to design precise and effective neuromodulation protocols. Computational models have thus become indispensable in advancing our understanding and control of neural responses to electrical stimulation. However, existing approaches suffer from computational bottlenecks, rendering them unsuitable for real-time applications, large-scale parameter sweeps, or sophisticated optimization. In this work, we introduce an approach for massively parallel estimation and optimization of neural fiber responses to electrical stimulation using machine learning techniques. By leveraging advances in high-performance computing and parallel programming, we present a surrogate fiber model that generates spatiotemporal responses to a wide variety of cuff-based electrical peripheral nerve stimulation protocols. We used our surrogate fiber model to design stimulation parameters for selective stimulation of pig and human vagus nerves. Our approach yields a several-orders-of-magnitude improvement in computational efficiency while retaining generality and high predictive accuracy, demonstrating its robustness and potential to enhance the design and optimization of peripheral neuromodulation therapies. Electricity can be used to stimulate the nervous system to treat diseases, and computer models are powerful tools for designing these therapies. Here, authors develop a model of neurons' responses to electricity that is accurate and thousands of times faster than the current industry standard. 

Highly efficient modeling and optimization of neural fiber responses to electrical stimulation

Highly varying patterns of electrostimulation (Dynamic Stimulation, DS) delivered to the dorsal cord through an epidural array with 18 independent electrodes transiently facilitate corticospinal motor responses, even after spinal injury. To partly unravel how corticospinal input are affected by DS, we introduced a corticospinal platform that allows selective cortical stimulation during the multisite acquisition of cord dorsum potentials (CDPs) and the simultaneous supply of DS. Firstly, the epidural interface was validated by the acquisition of the classical multisite distribution of CDPs and their input-output profile elicited by pulses delivered to peripheral nerves. Apart from increased EMGs, DS selectively increased excitability of the spinal interneurons that first process corticospinal input, without changing the magnitude of commands descending from the motor cortex, suggesting a novel correlation between muscle recruitment and components of cortically-evoked CDPs. Finally, DS increases excitability of post-synaptic spinal interneurons at the stimulation site and their responsiveness to any residual supraspinal control, thus supporting the use of electrical neuromodulation whenever the motor output is jeopardized by a weak volitional input, due to a partial disconnection from supraspinal structures and/or neuronal brain dysfunctions. 

Dynamic electrical stimulation enhances the recruitment of spinal interneurons by corticospinal input

Highly varying patterns of electrostimulation (Dynamic Stimulation, DS) delivered to the dorsal cord through an epidural array with 18 independent electrodes transiently facilitate corticospinal motor responses, even after spinal injury. To partly unravel how corticospinal input are affected by DS, we introduced a corticospinal platform that allows selective cortical stimulation during the multisite acquisition of cord dorsum potentials (CDPs) and the simultaneous supply of DS. Firstly, the epidural interface was validated by the acquisition of the classical multisite distribution of CDPs on the dorsal cord and their input-output profile elicited by pulses delivered to peripheral nerves. Apart from increased EMGs, DS selectively increased excitability of the spinal interneurons that first process corticospinal input, without changing the magnitude of commands descending from the motor cortex, suggesting a novel correlation between muscle recruitment and components of cortically-evoked CDPs. Finally, DS increases excitability of post-synaptic spinal interneurons at the stimulation site and their responsiveness to any residual supraspinal control, thus supporting the use of electrical neuromodulation whenever the motor output is jeopardized by a weak volitional input, due to a partial disconnection from supraspinal structures and/or neuronal brain dysfunctions.

/pdf/spinal-facilitation-of-descending-motor-input-3irj5l2o.pdf

Spinal facilitation of descending motor input

This paper presents a novel multi-channel stimulation backend with a multi-bit delta-sigma control loop, which enables precise adjustment of the stimulation current through modulation of the supply voltage. This minimizes the overhead voltage of series circuitry to the stimulation load and avoids the associated energy loss. Additionally, to address the bandwidth limitations commonly encountered in battery-less implants, we propose incorporating amplitude and duration scaling of the arbitrary stimulation waveform. The waveform is programmable with 64 7-bit samples and 4 scaling factors per channel, resulting in a minimum of 68% data reduction per channel compared to using the waveform without scaling. The proposed circuits are designed and simulated in 180nm BCD technology occupying a total silicon area of 9mm2. The fully integrated backend has a minimum compliance voltage of 8.5V and features a switched-capacitor multi-output DC-DC converter (MODDC) with pulse-skipping capability, a CMOS-only high-voltage (HV) multiplexer, and a unique HV H-bridge. Programming a sine-wave stimulus with a 4mA amplitude and a duration of 256μs achieved a signal-to-noise ratio of 40dB within a 10kHz bandwidth. For the same waveform, power efficiencies of 94% and 68% were observed without and with MODDC, respectively. Additionally, when programming constant-current stimuli ranging from 0.26mA to 4mA, high efficiencies of 78-97% and 23-79.4% were achieved without and with MODDC, respectively.

Delta-Sigma Control Loop For Energy-Efficient Electrical Stimulation with Arbitrary-Shape Stimuli

Novel neural stimulation protocols mimicking biological signals and patterns have demonstrated significant advantages as compared to traditional protocols based on uniform periodic square pulses. At the same time, the treatments for neural disorders which employ such protocols require the stimulator to be integrated into miniaturized wearable devices or implantable neural prostheses. Unfortunately, most miniaturized stimulator designs show none or very limited ability to deliver biomimetic protocols due to the architecture of their control logic, which generates the waveform. Most such designs are integrated into a single System-on-Chip (SoC) for the size reduction and the option to implement them as neural implants. But their on-chip stimulation controllers are fixed and limited in memory and computing power, preventing them from accommodating the amplitude and timing variances, and the waveform data parameters necessary to output biomimetic stimulation. To that end, a new stimulator architecture is proposed, which distributes the control logic over three component tiers - software, microcontroller firmware and digital circuits of the SoC, which is compatible with existing and future biomimetic protocols and with integration into implantable neural prosthetics. A portable prototype with the proposed architecture is designed and demonstrated in a bench-top test with various known biomimetic output waveforms. The prototype is also tested in vivo to deliver a complex, continuous biomimetic stimulation to a rat model of a spinal-cord injury. By delivering this unique biomimetic stimulation, the device is shown to successfully reestablish the connectivity of the spinal cord post-injury and thus restore motor outputs in the rat model.

/pdf/a-biomimetic-soc-based-neural-stimulator-for-novel-arbitrary-5cbmolqov7.pdf

A Biomimetic, SoC-Based Neural Stimulator for Novel Arbitrary-Waveform Stimulation Protocols.

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A Biomimetic, SoC-Based Neural Stimulator for Novel Arbitrary-Waveform Stimulation Protocols.