智能神经假体:心理与技术问题

Intelligent neural prostheses:psychophysical and technological issues

近年来,微系统技术、神经计算和生物系统等高新技术的发展,为新型植入式智能神经假体装置的发展、生产和应用提供了巨大的可能。其中一项特别引人关注的进展就是智能视觉假体。虽然通过中枢视觉系统到知觉域的机制还未完全了解,但视觉假体是通过电刺激视觉通路的不同部位来产生有用的视觉感受。许多年前,哲学和心理物理学研究都强调视觉感受可能是由不同的原因所引起。为了研制视觉假体,我们使用两张映射图来描述中枢视觉系统的功能是如何实现的。我们提出一种具有自学习功能且包括几个阶段的视网膜编码器(retina encoder,RE)。视网膜模块的RE模拟视网膜的功能并假定其实现了模式P1从物理域到神经域的映射操作M1。与此相对应的,中枢视觉系统模块则实现另一个映射操作M2,即M1在神经域的输出信号被转换到知觉域而产生视觉感受P2。在设定的迭代和基于感受的学习过程中,一名拥有正常视力的被试参与实验来提供由P1而产生P2的感性的相似性估计作为学习算法的输入,该算法反过来调整RE的参数矢量使得P2足够接近P1。更具体的说,全部RE时空滤波器可以借助遗传算法与被试交互式的实现调节。另一种视网膜编码器RE*使用特殊的时空滤波器组、决策树算法以及仿真的微小眼动算法组合构成。这种新编码器可以显著提高调节的结果。近期RE的研究集中在试图增强RE本身基于P1模式预处理的特性、模式分割、时间模式表达的选择性调整以及刺激电极簇的选择性控制。随着神经假体技术研究的不断深入和成熟,将会有越来越多的人受益。

Recent high-tech developments in microsystems technology, neural computation, and biosystems technology offer a significant potential for the development, production, and application of a new generation of implantable, learning neuroprosthetic devices. One particularly promising development concerns the international effort towards intelligent visual prostheses. The goal of visual prosthetics is the elicitation of useful visual percepts by means of electrical stimulation although the access to the perceptual domain via the central visual system is still not well understood. For many years, philosophical and psychophysical studies emphasized that visual percepts may be of very different origin. For the purpose of the development of a visual prosthesis, we interpret the function of the central visual system as a sequence of two mapping operations. Our development of a learning retina encoder （RE） evolved in several stages. The retina module RE serves as functional replacement of the retina and is assumed to perform a mapping operation MI of an input pattern PI from the physical domain onto the neural domain. In contrast, the adjacent central visual system module is assumed to perform another mapping operation M2 of the Ml-output signals in the neural domain onto the perceptual domain thus generation a visual percept P2. During the proposed iterative and perception-based learning process, a human subject （normally-sighted for developments; blind in future applications） provides a perceptual similarity estimate of P2 with respect to P1 as input for a learning algorithm, which in turn modifies the parameter vector of RE until P2 appears sufficiently similar to P1. More specifically, the ensemble of spatio-temporal （ST） filters of RE can be tuned in interaction with a human by means of genetic algorithms （GA）. Alternatively, a different retina encoder version, RE^＊employs a combination of specific ST filter classes as well as a decision-tree algorithm and simulated miniature eye movements. This approach promises significantly improved tuning results. More recent RE development attempt to further enhance the RE properties based on pattern pre-processing of P1, pattern segmentation, selective tuning of temporal pattern presentation, and selective control of clusters of stimulation electrodes. Future commercial success in the field of intelligent, implantable neuroprosthetic devices will significantly depend on the formation of ＇hybrid＇ teams of experts from biology, medicine, and technology. Intelligent technologybased therapies, which support or even functionally replace deficient parts of the human nervous system such as specific cases of blindness, deafness, paraplegia, Parkinson disease, and epilepsy are well under way or will become within reach within the next 5 to 10 years.