ERI: Memristor-based Neuromorphic Circuit Design for Closed-Loop Deep Brain Stimulation

帕金森病 (PD) 是一种脑部疾病，每年影响全球数百万患者。 PD 患者会出现运动症状，包括不受控制的颤抖或肌肉僵硬，可能会感觉紧绷且难以移动。深部脑刺激（DBS）是治疗帕金森病运动症状的有效疗法。 DBS 是一种外科手术，涉及在胸部放置信号发生器，向大脑的特定区域发送电脉冲。来自信号发生器的这些电脉冲通过影响大脑中的细胞来控制运动症状。 DBS 系统改变了患者的生活，但连续且严格的电脉冲会导致不必要的副作用，例如可能阻塞血流。最近，科学家开发了一种称为闭环 DBS (CL-DBS) 的新型 DBS 系统，其中发生器可以根据 PD 症状将各种电脉冲发送回大脑，以避免副作用。 CL-DBS 系统面临的一个挑战是它需要一台功能强大的计算机来生成预期的各种电脉冲。然而，这些功能强大的计算机由于体积庞大而无法放置在患者的胸部。为了解决这个问题，该项目旨在设计一种称为神经形态芯片的新型计算机。该芯片模仿人脑，利用小尖峰信号进行计算，实现高能效。这种新型神经形态芯片将使CL-DBS变得更智能、更小、更轻，使所有PD患者受益匪浅。该项目为来自密歇根州上半岛铜乡地区的大学生和高中生提供了参与神经科学、帕金森病、大脑康复、人工智能和微芯片设计等跨学科研究的独特机会。 该项目旨在设计一种由电子神经元和忆阻突触组成的神经形态 CL-DBS 芯片，能够适应用户症状，同时大幅降低功耗和设备尺寸。 PD 症状的严重程度通过大脑的 β 振荡来指示。两个尖峰神经网络（SNN）将被放置在闭环系统的前馈和反馈分支上，以识别PD症状并相应地生成刺激信号。这些 SNN 中的神经元和突触将使用互补金属氧化物半导体 (CMOS) 技术和忆阻器来实现。该项目将重点关注两项任务：（1）忆阻突触设计； (2)电子神经元设计。此外，还将开发带通滤波器、低噪声放大器和读/写电路等外围电路。该芯片将通过与使用不同计算硬件（包括图形处理单元（GPU）和现场可编程门阵列（FPGA））的其他 CL-DBS 系统进行比较来进行进一步评估，基于 1) 功耗； 2）响应时间； 3）芯片面积。这项关于神经形态医疗芯片的研究将通过减小其尺寸、重量和能量预算，同时使它们更加智能和适应性更强，从而显着有利于节能和智能植入式医疗设备的开发。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Parkinson's disease (PD) is a brain disorder affecting millions of patients worldwide each year. PD patients experience motor symptoms including uncontrolled shaking or rigid muscles that may feel tight and difficult to move. Deep brain stimulation (DBS) is an effective therapy used to treat motor symptoms of PD. DBS is a surgical procedure that involves placing a signal generator in the chest to send electrical pulses to a specific region of the brain. These electrical pulses from the signal generator control motor symptoms by affecting the cells in the brain. The DBS system is life-changing for patients, but the continuous and rigid electrical pulses cause unwanted side effects, such as potentially blocking blood flow. Recently, scientists have developed a new DBS system called Closed-Loop DBS (CL-DBS) where the generator can send various electrical pulses back to the brain depending on PD symptoms to avoid side effects. One challenge for a CL-DBS system is it requires a powerful computer to generate the expected and various electrical pulses. However, these powerful computers cannot be placed in the chest of patients due to their large size. To solve this issue, this project aims to design a new type of computer called a neuromorphic chip. This chip mimics human brains by using small spiking signals for calculations to realize high energy efficiency. This new neuromorphic chip will make CL-DBS smarter, smaller, and lighter, greatly benefiting all PD patients. The project provides a unique opportunity for college and high school students from the Copper Country region in the Upper Peninsula of Michigan to participate in interdisciplinary research on neuroscience, Parkinson’s disease, brain rehabilitation, artificial intelligence, and microchip design. The project aims to design a neuromorphic CL-DBS chip consisting of electronic neurons and memristive synapses that adapts to user symptoms while substantially lowering power consumption and device size. The severity of PD symptoms is indicated by beta oscillations from the brain. Two Spiking Neural Networks (SNN) will be placed at the feedforward and feedback branches of the closed-loop system to recognize PD symptoms and generate stimulation signals accordingly. The neurons and synapses in these SNNs will be implemented using complementary metal-oxide semiconductor (CMOS) technology and memristors. The project will focus on two tasks: (1) memristive synapse design; and (2) electronic neuron design. Additionally, peripheral circuitries, including bandpass filters, low-noise amplifiers, and reading/writing circuits, will be developed. The chip will be taped out for further evaluation by comparing it with other CL-DBS systems using different computational hardware, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), based on 1) power consumption; 2) response time; 3) chip area. This research on neuromorphic medical chips will significantly benefit the development of energy-efficient and smart implantable medical devices by reducing their size, weight, and energy budget while making them more intelligent and adaptive.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.