Ultra Low Power Implantable Platform for Next Generation Neural Interfaces

半个多世纪以来，科学家们使用耗电的大型设备，记录了清醒动物大脑中执行特定行为的神经元产生的微小电势。这些实验对大脑如何表示感觉信息并将其转换为控制有目的运动的信号产生了深刻的见解，并揭示了这个复杂的系统是如何受到神经损伤和疾病的影响的。然而，直到最近，神经科医生可用的治疗途径一直局限于全身药物应用和神经外科损伤等粗略干预措施。近年来，已开发出小型电子设备，通过植入神经系统的小电极提供特定模式的刺激。深脑刺激器和人工耳蜗等设备已经帮助了全球数以千计的患者。下一代神经植入物将使用类似的电极来检测神经元的活动，为治疗目前沉重的临床负担的疾病铺平道路。例如，通过利用大脑运动区神经元的活动来控制肌肉的电刺激，就有可能恢复因脊髓损伤而瘫痪的患者的随意运动。然而，尽管电极技术取得了长足的进步，但目前我们在单个神经元水平上连接数字微电子与大脑的能力严重有限。每个电极检测来自其附近多个细胞的信号，它们产生的微小、短暂的“尖峰”事件在背景噪音下可能很难区分。为了解决这个问题，我们组建了一个跨学科团队，在三个关键领域拥有专业知识：检测和分类尖峰事件所需的计算算法，执行实时、可靠的尖峰识别的低功率集成电路，以及使用神经植入记录大脑长期活动的技术，以便评估现实世界的表现。其目的是提供一种平台技术，将来自电极的原始信号转换为适合于传统数字微电子设备后续处理的已识别尖峰事件流，并适合整合到一系列无线可植入设备中。这种技术的出现将彻底改变治疗各种神经系统疾病的设备的发展。

For over half a century scientists have recorded the tiny electrical potentials generated by neurons in the brains of awake animals performing specific behaviours, using large racks of power-hungry equipment. These experiments have yielded profound insights into how sensory information is represented and transformed by the brain into the signals that control purposeful movements, as well as revealing how this complex system is affected by neurological injuries and disease. However, until recently the therapeutic avenues available to neurologists have been limited to gross interventions such as systemic drug applications and neurosurgical lesions.In recent years, small electronic devices have been developed that deliver specific patterns of stimulation via small electrodes implanted in the nervous system. Devices such as Deep Brain Stimulators and Cochlear Implants have helped many thousands of patients worldwide. The next generation of neural implants will use similar electrodes to detect the activity of neurons, paving the way for new treatments for conditions that currently weigh a heavy clinical burden. For example, by using the activity of neurons in motor areas of the brain to control electrical stimulation of muscles, it is possible that voluntary movements could be restored to patients paralysed by spinal cord injuries. However, despite considerable advances in electrode technologies, our ability to interface digital microelectronics with the brain at the level of individual neurons is at present severely limited. Each electrode detects the signal from multiple cells in its vicinity, and the small, brief 'spike' events they generate can be hard to distinguish beneath the background noise.To solve this problem we have assembled a cross-disciplinary team with expertise in three key areas: the computational algorithms required to detect and sort spike events, low power integrated electronics to perform real-time, reliable spike identification, and the techniques to record long-term activity from the brain using neural implants in order to evaluate real-world performance. The aim is to deliver a platform technology that will convert the raw signal from electrodes into a stream of identified spike events suitable for subsequent processing by conventional digital microelectronics, and be suitable for incorporation into a range of wireless, implantable devices. The availability of such a technology would revolutionise the development of devices to treat a wide variety of nervous system disorders.