globalchange  > 影响、适应和脆弱性
项目编号: 1351112
项目名称:
CAREER:Manipulating Neural Plasticity to Enable Brain-Computer Interface Learning
作者: Alan Dorval
承担单位: University of Utah
批准年: 2013
开始日期: 2014-05-01
结束日期: 2019-04-30
资助金额: USD456001
资助来源: US-NSF
项目类别: Standard Grant
国家: US
语种: 英语
特色学科分类: Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
英文关键词: brain ; brain-computer interface ; brain-computer ; motor learning ; career ; basal ganglia ; external device ; learning-control ; brain process ; rodent brain ; neural plasticity ; brain center ; neural learning mechanism ; project ; brain-computer-interface device ; deep brain ; latent learning machinery
英文摘要: PI: Dorval, Alan D.
Proposal Number: 1351112
Institution: University of Utah
Title: CAREER: Manipulating Neural Plasticity to Enable Brain-Computer Interface Learning

The overall goal of this project is to develop computational systems that can teach the brain how to interface with them in both directions. Machinery in the brain has evolved to learn complex motor skills that endure for a lifetime, like tying a shoelace or riding a bike. This project will examine the feasibility of systems designed to coopt this latent learning machinery, to teach neural tissue to manage a brain-computer interface to control external devices, like a motorized wheel chair or a robotic arm. If neurons in a small area could be taught to generate a coordinated signal, wearable devices could use those robust signals to reliably control external devices. To do this, electrical stimulation will be paired to the learning-control and motor-control centers of the rodent brain, teaching neurons to synchronize activation with their neighbors. Once neighboring neurons can generate synchronous activity reliably, the rodents will be let use those signals to control external devices. Coopting neural learning mechanisms will eventually enable long-term, reliable, and robust, brain-computer interfaces for neural rehabilitation of people with disabilities. To expand the pool of individuals contributing to this nascent field of neural engineering, this project will teach relevant experimentation to undergraduates. In a standard BioSystems Engineering course, the PI will add a laboratory component in which students learn to manipulate the nervous systems of insects and other invertebrates, to control their behaviors. These student experiences should be engaging and memorable and should translate to a broader understanding of and an interest in careers in neural engineering.

Motor skill acquisition is mediated via brain processes that engage both motor control (M1) and motor learning (basal ganglia) brain centers. Efferent basal ganglia is a preferred surgical target for deep brain stimulating electrodes, to alleviate symptoms of parkinsonism and dystonia, and tens of thousands of multi-contact leads have been safely implanted in humans. Given their role in motor learning, the efferent basal ganglia are ideal sites from which to modify cortical connectivity. The research objective of this proposal is to determine if plasticity in the basal ganglia-thalamo-cortical loop can be coopted to teach the brain to generate large, easily-detected, neural-field activity that could then be used to drive brain-computer-interface devices. This objective will be achieved through 3 tasks using a rodent model. First, the ability of coupled electrical stimulation to modify the connection strengths between the motor learning (basal ganglia) and motor control (motor cortex) centers of the brain will be tested. In parallel, a closed-loop system will be built to record selective activity from, and provide arbitrary stimulation to, 64 channels simultaneously and independently with sub-millisecond latency. Finally, with real-time feedback and unit-activity detection in the motor cortex, the brain will be taught to amplify the unit activity to correlates to natural behaviors, into large field events that can be recorded reliably for years, and thus drive body-external devices.
资源类型: 项目
标识符: http://119.78.100.158/handle/2HF3EXSE/96951
Appears in Collections:影响、适应和脆弱性
气候减缓与适应

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Alan Dorval. CAREER:Manipulating Neural Plasticity to Enable Brain-Computer Interface Learning. 2013-01-01.
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