Silva Gabriel A
Departments of Bioengineering and Neurosciences, Center for Engineered Natural Intelligence, University of California San Diego, La Jolla, CA, United States.
Front Neurosci. 2018 Nov 16;12:843. doi: 10.3389/fnins.2018.00843. eCollection 2018.
A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable "smart" nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG). Advances in computation, hardware, and algorithms that learn and adapt in a contextually dependent way will be able to leverage the capabilities that nanoengineering offers the design and functionality of BMI. We explore the enabling capabilities that these devices may exhibit, why they matter, and the state of the technologies necessary to build them. We also discuss a number of open technical challenges and problems that will need to be solved in order to achieve this.
多种技术能力的融合为机器学习和人工智能创造了机会,使其能够实现“智能”纳米工程脑机接口(BMI)。新一代技术将能够以支持情境学习和适应不断变化的功能需求的方式与大脑进行通信。这既适用于旨在恢复神经功能的侵入性技术,如神经假体的情况,也适用于由脑电图(EEG)等信号实现的非侵入性技术。以情境依赖方式进行学习和适应的计算、硬件和算法方面的进步,将能够利用纳米工程为脑机接口的设计和功能所提供的能力。我们探讨了这些设备可能展现的赋能能力、它们为何重要以及构建这些设备所需技术的现状。我们还讨论了为实现这一目标需要解决的一些开放性技术挑战和问题。