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Advanced Algorithms for Neural Prosthetic Systems

神经修复系统的先进算法

基本信息

批准号：
EP/H019472/1
负责人：
Zoubin Ghahramani
金额：
$ 51.95万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FH019472%2F1
关键词：
Advanced Algorithms Neural Prosthetic Systems

项目摘要

Our seemingly effortless ability to make coordinated movements belies the sophisticated computational machinery at work in our nervous system. In recent years, the field of neuroscience has been dramatically expanding the complexity of its data acquisition technologies and experiments. This technological development has created a preponderance of valuable experimental data, but the analytical methods required to deeply interrogate this data have not yet been developed. Simultaneously, the last decade has seen major advances in the fields of computational statistics, data analysis techniques, and machine learning. Research in these areas has enabled investigation into and understanding of previously uninterpretable data.This proposal seeks to bring together key research from these two fields to significantly advance the scientifically and medically important application of neural prosthetic systems, which seeks to improve greatly the quality of life of hundreds of thousands of severely disabled human patients worldwide. Debilitating diseases like Amyotrophic Lateral Sclerosis can leave a human without voluntary motor control. However, in most cases, the brain itself remains intact and has normal function. The same is true with spinal cord injuries that result in severe paralysis. In fact, tetrapalegic patients list ``regaining arm/hand control'' as the top priority for improving their quality of life, as regaining this function would allow significant patient independence. To address this priority, neural prosthetic systems seek to access the information in the brain and use that information to control a prosthetic device such as a robotic arm or a computer cursor. There are many medical, scientific, and engineering challenges in developing such a system, but all neural prosthetic systems share in common a decoding algorithm. Decoding algorithms map neural activity into physical commands such as parameters for controlling a robotic arm. Current decoding approaches have shown exciting proofs of concept, but there are a number of shortcomings that must be addressed before the field produces a clinically viable prosthetic device with speed and accuracy comparable to a healthy human arm. Our research programme will use advanced statistical and machine learning technologies to create algorithms that can decode neural activity with higher precision that previously seen. We have identified several opportunities for meaningful improvement, from incorporating the statistics of natural reaching to validating these algorithms in a realistic online setting. Taken together, these algorithmic developments should help create a much higher quality neural prosthetic device.

我们看似毫不费力地进行协调运动的能力掩盖了我们神经系统中复杂的计算机制的运作。近年来，神经科学领域的数据采集技术和实验的复杂性急剧扩大。这项技术的发展创造了大量有价值的实验数据，但深入研究这些数据所需的分析方法尚未开发出来。同时，过去十年在计算统计、数据分析技术和机器学习领域取得了重大进展。这些领域的研究使人们能够调查和理解以前无法解释的数据。该提案旨在汇集这两个领域的关键研究，以显着推进神经假体系统在科学和医学上的重要应用，从而大大提高全世界数十万严重残疾人类患者的生活质量。肌萎缩侧索硬化症等使人衰弱的疾病会使人失去自主运动控制能力。然而，在大多数情况下，大脑本身保持完整并具有正常功能。导致严重瘫痪的脊髓损伤也是如此。事实上，四肢瘫痪患者将“恢复手臂/手的控制”列为改善生活质量的首要任务，因为恢复这种功能将使患者能够显着独立。为了解决这个问题，神经假肢系统试图访问大脑中的信息，并使用该信息来控制假肢设备，例如机械臂或计算机光标。开发这样的系统面临许多医学、科学和工程挑战，但所有神经修复系统都有一个共同的解码算法。解码算法将神经活动映射为物理命令，例如用于控制机械臂的参数。目前的解码方法已经显示出令人兴奋的概念证明，但在现场生产出速度和精度可与健康人臂相媲美的临床上可行的假肢装置之前，必须解决许多缺点。我们的研究计划将使用先进的统计和机器学习技术来创建能够以比以前更高的精度解码神经活动的算法。我们已经确定了一些有意义的改进机会，从合并自然到达的统计数据到在现实的在线环境中验证这些算法。总而言之，这些算法的发展应该有助于创建更高质量的神经假肢设备。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Linear Dimensionality Reduction: Survey, Insights, and Generalizations

线性降维：调查、见解和概括

DOI：
10.48550/arxiv.1406.0873
发表时间：
2014
期刊：
arXiv e-prints
影响因子：
0
作者：
Cunningham John P.
通讯作者：
Cunningham John P.

Gaussian Processes for time-marked time-series data

DOI：
发表时间：
2012-03
期刊：
影响因子：
0
作者：
J. Cunningham;Zoubin Ghahramani;C. Rasmussen
通讯作者：
J. Cunningham;Zoubin Ghahramani;C. Rasmussen

Scaling multidimensional Gaussian Processes using projective additive approximations

使用投影加法近似缩放多维高斯过程

DOI：
发表时间：
期刊：
影响因子：
0
作者：
Elad Gilboa (Author)
通讯作者：
Elad Gilboa (Author)

Cortical preparatory activity: representation of movement or first cog in a dynamical machine?

DOI：
10.1016/j.neuron.2010.09.015
发表时间：
2010-11-04
期刊：
NEURON
影响因子：
16.2
作者：
Churchland, Mark M.;Cunningham, John P.;Kaufman, Matthew T.;Ryu, Stephen I.;Shenoy, Krishna V.
通讯作者：
Shenoy, Krishna V.

Neural population dynamics during reaching.

DOI：
10.1038/nature11129
发表时间：
2012-07-05
期刊：
NATURE
影响因子：
64.8
作者：
Churchland, Mark M.;Cunningham, John P.;Kaufman, Matthew T.;Foster, Justin D.;Nuyujukian, Paul;Ryu, Stephen I.;Shenoy, Krishna V.
通讯作者：
Shenoy, Krishna V.