Bioelectronic medicine treats chronic diseases by sensing, processing, and modulating the electronic signals produced in the nervous system of the human body, labeled ‘neural signals’. While electronic circuits have been used for several years in this domain, the progress in microelectronic technology is now allowing increasingly accurate and targeted solutions for therapeutic benefits. For example, it is now becoming possible to modulate signals in specific nerve fibers, hence targeting specific diseases. However, to fully exploit this approach it is crucial to understand what aspects of the nerve signals are important, what is the effect of the stimulation, and what circuit designs can best achieve the desired result. Neuromorphic electronic circuits represent a promising design style for achieving this goal: their ultra-low power characteristics and biologically plausible time constants make them the ideal candidate for building optimal interfaces to real neural processing systems, enabling real-time closed-loop interactions with the biological tissue. In this paper, we highlight the main features of neuromorphic circuits that are ideally suited for interfacing with the nervous system and show how they can be used to build closed-loop hybrid artificial and biological neural processing systems. We present examples of neural computational primitives that can be implemented for carrying out computation on the signals sensed in these closed-loop systems and discuss the way to use their outputs for neural stimulation. We describe examples of applications that follow this approach, highlight open challenges that need to be addressed, and propose actions required to overcome current limitations.

Neuromorphic bioelectronic medicine for nervous system interfaces: from neural computational primitives to medical applications

Spinal cord stimulation (SCS) is an effective treatment for chronic, intractable neuropathic pain. There have been relatively few high-level studies that suggest its unequivocal use. The decay of s...

Spinal cord stimulation for the treatment of neuropathic pain: expert opinion and 5-year outlook

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.

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

Electrical stimulation using non-periodic biomimetic stimulation pattern has been shown to be effective in various critical biomedical applications. However, the existing programmable stimulators that support this protocol are non-portable and have architectures that are not translatable to wearable or implantable applications. In this work, we present a 32-channel neural stimulator system based on an implantable System-On-Chip (SoC) that addresses these technological challenges. The system is designed to be portable, powered by a single battery, wirelessly controlled, and versatile to perform concurrent multi-channel stimulation with independent arbitrary waveforms. The experimental results demonstrate multi-channel stimulation mimicking electromyography (EMG) waveforms and randomly-spaced stimulation pulses mimicking neuronal firing patterns. This compact and highly flexible prototype can support various neuromodulation researches and animal studies and serves as a precursor for the development of the next generation implantable biomimetic stimulator.

A Novel Biomimetic Stimulator System for Neural Implant

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

A Novel Biomimetic Stimulator System for Neural Implant