神经电生理微电极阵列检测系统研制

该文设计了基于微电极阵列的16通道神经电生理信号检测系统。检测系统由硬件和软件两部分组成,其中硬件部分可分为以下3个模块:微电极阵列接口模块,用于实现微电极阵列和检测系统的可靠连接;多通道信号放大模块,用于对微弱电生理信号进行提取并放大至合适的幅度;数据采集模块,对放大后的电生理信号进行高速数据采集并通过USB2.0接口和计算机相连。软件部分采用多线程、多缓存等技术保证对信号的实时观测和分析。对检测系统的主要参数进行了测试,并结合实验室自制神经微电极阵列对SD大鼠海马区脑切片进行神经电生理信号的检测。系统的输入噪声Vrms2 V,放大倍数为1000倍,频率带宽范围为10~3000 Hz,并且能够检测到放电幅度为20 V左右的神经电生理信号。该文针对微电极阵列神经电生理信号检测中的技术难点,从硬件和软件设计上保证微弱信号的提取,检测系统的分辨率可达0.6 V,各项参数能够满足神经电生理信号的检测需要。     

基于Parylene的柔性微电极阵列微加工工艺研究

基底集成的柔性微电极阵列(MEAs)从一个全新的角度演绎了植入式神经系统,对神经进行电刺激并记录神经电信号.以一种新型聚合物材料聚对二甲苯(parylene)为基底,制备出了用于神经接口的柔性神经微电极阵列.采用MEMS加工技术,设计了一种基于parylene柔性神经微电极阵列的加工工艺方法,并讨论了在流片过程中的关键问题,如掩膜层的选择、电极的剥离及焊接与封装等.该柔性微电极阵列在用于视觉假体的神经接口方面具有独特的应用优势.     

植入式双面柔性神经微电极制作方法的研究

为了减小植入损伤,提高神经微电极刺激或记录的空间密度,提出了一种基于聚合物基质材料的植入式双面柔性神经微电极的制作工艺和方法.该方法采用光敏型聚酰亚胺(Durimide 7510)作为微电极的基质材料,通过粘结剂局部键合和电化学腐蚀牺牲层的方法实现电极翻转以制作反面电极.同时采用了一种基于应力集中的凹槽结构,以保证所得微电极形状的规整性.微电极的SEM和电学性能测试结果表明,微电极的结构形貌和电学性能满足植入式电极的要求.     

柔性神经微电极阵列设计及微加工工艺研究

采用聚对二甲苯(parylene)作为基底,通过对柔性神经微电极阵列结构、引线排布、电极材料等进行优化选择和设计,并采用微机电加工工艺(MEMS),设计并研制出了2×8柔性神经微电极阵列,旨在为视网膜神经接口电极的研究开发奠定基础.     

微电极阵列大脑电信号检测技术的进展

 微电极阵列记录技术提供了一种理想的神经电生理检测手段,可以同时获得大量神经细胞的电活动信息,对于深入研究大脑神经细胞及其网络的工作机制,开发新的神经修复技术具有重要的意义.近年来迅速发展的用于在体神经信号检测的微电极阵列主要有两种类型:Utah电极和Michigan电极.本文将介绍它们的制造工艺、结构、特点、应用进展,及其用于检测和分析神经细胞场电位和胞外动作电位的方法和原理;并且,分析和探讨微电极阵列在电极制造、信号记录和信号分析等方面亟待解决的一些问题和今后的发展方向,以促进我国在微电极阵列开发和应用领域的快速发展.     

基于SOI硅片的硅微电极制作与评估

采用微机电系统(MEMS)工艺方法制作了基于SOI衬底的七通道硅微电极,用于视神经视觉修复. 通过噪声分析确定了硅微电极的金属暴露位点的几何尺寸. 优化设计了硅微电极的几何结构,以便于减小植入损伤. 阻抗测试结果表明,当测试电压为50mVpp时,1kHz频率下,微电极的单通道阻抗为2.3MΩ,适用于神经电信号记录. 在体实验结果表明,动物初级视皮层记录到的神经电信号幅度为8μV.     

基于SOI硅片的硅微电极制作与评估

采用微机电系统(MEMS)工艺方法制作了基于SOI衬底的七通道硅微电极,用于视神经视觉修复.通过噪声分析确定了硅微电极的金属暴露位点的几何尺寸.优化设计了硅微电极的几何结构,以便于减小植入损伤.阻抗测试结果表明,当测试电压为50mVpp时,1kHz频率下,微电极的单通道阻抗为2.3MQ,适用于神经电信号记录.在体实验结果表明,动物初级视皮层记录到的神经电信号幅度为8μV.     

导电岛微电极系统中纳米线介电组装行为研究

为了获得导电岛微电极系统中纳米线的介电组装特性,基于平面微电极对和导电岛微电极系统,进行了两种系统中纳米线操控的对比实验。分别建立了平面微电极对和导电岛微电极系统的纳米线介电组装模型,探究了两种模型下的纳米线从初始位置到最终桥接上微间隙过程中的运动轨迹;分析了导电岛微电极系统中纳米线所受的介电泳力、交流电热流以及两者合作用的电动力学行为。导电岛微电极系统对纳米线有着较强的介电俘获作用,导电岛的加入能够让纳米线更好地俘获到微间隙;同时纳米线的介电组装会受到频率的影响,当频率达到翻转频率,在微间隙上方产生的微流体漩涡能够把远场区域纳米线输送到组装区,使得纳米线受到正介电泳力的作用而被组装至微间隙。     

基于CMOS工艺的抗光噪声神经微电极

为了减小神经电极的宽度,提高电极在光照下的抗噪声干扰能力,提出了一种基于0.18μm CMOS工艺的抗光噪声神经微电极。采用CMOS的多层布线代替传统电极导线的单层排布,并将电极衬底接地以有效减小光噪声。阐述了基于CMOS工艺的神经电极结构设计、制备过程与结构表征,并对所制备的神经电极进行了电化学阻抗测试和光噪声测试。该神经电极宽度仅为70μm,实验证明:1kHz频率下电极的阻抗一致性好,且在1mW/mm2的光遗传常用光辐照下,该电极的噪声电压仅为0.07~0.08mV,远低于传统硅电极12~13mV的噪声幅值。结果表明,基于CMOS工艺的神经电极抗光噪声能力远优于传统硅电极,对硅基微电极在光遗传中的应用具有重要意义。     

基于光学标测技术和MEA技术的植物电研究概述

植物电信号的研究进展很大程度上依赖于测量技术的发展。目前对于微电极等测量技术而言,多方面因素的限制使得同时测量2个以上细胞的电活动基本无法实现,因此对植物群体细胞电活动规律的研究进展较小。本文介绍了2种可以同时对植物群体细胞电活动进行测量并具有空间分辨率的测量技术-依赖于电压敏感染料的光学标测技术和微电极阵列技术。分别介绍了光学标测技术的测量原理、电压敏感染料的特性、系统构成、信号提取方法等内容,并列举了在植物细胞电信号测量方面的应用实例,概述了影响植物电信号光学标测的因素;阐述了微电极阵列技术的系统组成、阵列微电极的特征、测量依据及模型、影响测量的因素,并对阵列微电极在玉米幼根测量中的应用做了简要介绍。最后对这2种测量技术在植物电信号测量中存在的一些问题进行了讨论。     

Overcoming endogenous constraints on neuronal regeneration

One of the grand challenges in neuroengineering is to stimulate regeneration after central nervous system (CNS) or peripheral nervous system (PNS) injury to restore function. The state of the art today is that PNS injuries heal to a limited extent, whereas CNS injuries are largely intractable to regeneration. In this context, we examine the underlying biochemical and cellular constraints on endogenous healing of neural tissues. Identification and characterization of endogenous "rate-limiting" processes that constrain regeneration would allow one to craft solutions to overcome critical impediments for accelerated healing. It is increasingly evident that biochemical pathways triggered by the nature and duration of injury-triggered inflammatory response may determine the endogenous constraints and subsequently determine regenerative fate. In this paper, critical endogenous constraints of PNS and CNS regeneration are identified, and the effects of modulating the phenotypes of immune cells on neuronal regeneration are discussed.     

Customizing MRI-Compatible Multifunctional Neural Interfaces through Fiber Drawing

Fiber drawing enables scalable fabrication of multifunctional flexible fibers that integrate electrical, optical, and microfluidic modalities to record and modulate neural activity. Constraints on thermomechanical properties of materials, however, have prevented integrated drawing of metal electrodes with low-loss polymer waveguides for concurrent electrical recording and optical neuromodulation. Here, two fabrication approaches are introduced: 1) an iterative thermal drawing with a soft, low melting temperature (T_m) metal indium, and 2) a metal convergence drawing with traditionally non-drawable high T_m metal tungsten. Both approaches deliver multifunctional flexible neural interfaces with low-impedance metallic electrodes and low-loss waveguides, capable of recording optically-evoked and spontaneous neural activity in mice over several weeks. These fibers are coupled with a light-weight mechanical microdrive (1 g) that enables depth-specific interrogation of neural circuits in mice following chronic implantation. Finally, the compatibility of these fibers with magnetic resonance imaging is demonstrated and they are applied to visualize the delivery of chemical payloads through the integrated channels in real time. Together, these advances expand the domains of application of the fiber-based neural probes in neuroscience and neuroengineering.     

Integration of perception and reasoning in fast neural modules

Artificial neural systems promise to integrate symbolic and subsymbolic processing to achieve real-time control of physical systems. Two potential alternatives exist. In one, neural nets can be used to front-end expert systems. The expert systems, in turn, are developed with varying degrees of parallelism, including their implementation in neural nets. In the other, rule-based reasoning and sensor data can be integrated within a single hybrid neural system. The hybrid system reacts as a unit to provide decisions (problem solutions) based on the simultaneous evaluation of data and rules. This paper discusses a model hybrid system based on the fuzzy cognitive map (FCM). The operation of the model is illustrated with the control of a hypothetical satellite that intelligently alters its attitude in space in response to an intersecting micrometeorite shower.     

Identification and control of continuous-time nonlinear systems via dynamic neural networks

In this paper, we present an algorithm for the online identification and adaptive control of a class of continuous-time nonlinear systems via dynamic neural networks. The plant considered is an unknown multi-input/multi-output continuous-time higher order nonlinear system. The control scheme includes two parts: a dynamic neural network is employed to perform system identification and a controller based on the proposed dynamic neural network is developed to track a reference trajectory. Stability analysis for the identification and the tracking errors is performed by means of Lyapunov stability criterion. Finally, we illustrate the effectiveness of these methods by computer simulations of the Duffing chaotic system and one-link rigid robot manipulator. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for control of unknown continuous-time nonlinear systems with output disturbance noise.     

Input-to-State Stability of Memristive Neural System with Time Delays

Memristive neural systems are a ground breaking concept that is helping us understand the behavior of electronic brain. In this paper, a general class of memristive neural systems with time delays is formulated and investigated. Several succinct criteria are given to ascertain the input-to-state stability via nonsmooth analysis and control theory. These conditions, which can be directly derived from the parameters of the system, are easily verified. The obtained results extend some previous works on conventional neural systems. A numerical example is provided to show the efficiency of the proposed approach.     

Accurate contour control of mechatronic servo systems usingGaussian networks

This paper presents a method of contour control of mechatronic servo systems by using neural networks. The neural network learns the inverse dynamics of the mechatronic servo system. The input data for the mechatronic servo systems are modified from objective trajectories by using the neural network. The Gaussian network is adopted to construct the inverse dynamics of the mechatronic servo system because the Gaussian function is well defined, and its structure and initial parameters can be systematically selected such that the initial network approximates the inverse dynamics of the mechatronic servo system. The actual input/output data of the mechatronic servo system are used for the learning of the Gaussian network. Effectiveness of the proposed method is assured by experimental results of contour control of an X-Y table     

Neural network for the reliability analysis of simplex systems

A new approach to the reliability analysis, based on neural networks, is introduced in this paper. The reliability analysis of a simple nonredundant digital system, Simplex System, with repair is used to illustrate the neural network approach. The discrete-time Markov model of simplex systems is realized using feed-forward recursive neural network. The energy function and update equations for the weights of the neural network are estabilished such that the network converges to the desired reliability of the simplex system under design. The failure rate and repair rate, satisfying the desired reliability, are extracted from the neural weights at convergence. The obtained results are verified by the conventional approach.     

Adaptive neural network feedback control of a passive line-of-sight stabilization system

An adaptive neural network full-state feedback controller has been designed and applied to the passive line-of-sight (LOS) stabilization system. Model reference adaptive control (MRAC) is well established for linear systems. However, this method cannot be utilized directly since the LOS system is nonlinear in nature. Utilizing the universal approximation property of neural networks, an adaptive neural network controller is presented by generalizing the model reference adaptive control technique, in which the gains of the controller are approximated by neural networks. This removes the requirement of linearizing the dynamics of the system, and the stability properties of the closed-loop system can be guaranteed.     

局域自适应子波高斯神经网络综合分类系统

本文提出了一种用于船舶噪声分类的局域自适应子波高斯神经网络综合分类系统。该系统融合了两种特征提取和分类方法,即自适应子波神经网络和自适应高斯神经网络分类器,并利用网络局域化使得系统具有追加学习的能力。通过对实际的三类船舶噪声进行分类识别,结果令人满意,证明了该方法的优越性和工程应用前景。     

A recurrent neural-network-based real-time learning controlstrategy applying to nonlinear systems with unknown dynamics

In this paper, the authors present a real-time learning control scheme for unknown nonlinear dynamical systems using recurrent neural networks (RNNs). Two RNNs, based on the same network architecture, are utilized in the learning control system. One is used to approximate the nonlinear system, and the other is used to mimic the desired system response output. The learning rule is achieved by combining the two RNNs to form the neural network control system. A generalized real-time iterative learning algorithm is developed and used to train the RNNs. The algorithm is derived by means of two-dimensional (2-D) system theory that is different from the conventional algorithms that employ the steepest optimization to minimize a cost function. This paper shows that an RNN using the real-time iterative learning algorithm can approximate any trajectory tracking to a very high degree of accuracy. The proposed learning control scheme is applied to numerical problems, and simulation results are included. The results are very promising, and this paper suggests that the 2-D system theory-based RNN learning algorithm provides a new dimension in real-time neural control systems