School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA.
CINAC, HM Puerta del Sur, Hospitales de Madrid, 28938 Móstoles, and CEU-San Pablo University, 28003 Madrid, Spain; Neural Bioengineering Group, Hospital Nacional de Parapléjicos, SESCAM, Finca la Peraleda s/n, 45071 Toledo, Spain.
Neuron. 2015 Apr 8;86(1):55-67. doi: 10.1016/j.neuron.2015.03.036.
The field of invasive brain-machine interfaces (BMIs) is typically associated with neuroprosthetic applications aiming to recover loss of motor function. However, BMIs also represent a powerful tool to address fundamental questions in neuroscience. The observed subjects of BMI experiments can also be considered as indirect observers of their own neurophysiological activity, and the relationship between observed neurons and (artificial) behavior can be genuinely causal rather than indirectly correlative. These two characteristics defy the classical object-observer duality, making BMIs particularly appealing for investigating how information is encoded and decoded by neural circuits in real time, how this coding changes with physiological learning and plasticity, and how it is altered in pathological conditions. Within neuroengineering, BMI is like a tree that opens its branches into many traditional engineering fields, but also extends deep roots into basic neuroscience beyond neuroprosthetics.
侵入式脑机接口 (BMI) 领域通常与旨在恢复运动功能丧失的神经假体应用相关。然而，BMI 也是解决神经科学基本问题的强大工具。BMI 实验的观察对象也可以被视为自身神经生理活动的间接观察者，并且观察神经元与（人工）行为之间的关系可以真正具有因果关系，而不是间接相关。这两个特征违背了经典的主客体二分法，使 BMI 特别适合研究信息如何实时被神经回路编码和解码、这种编码如何随着生理学习和可塑性而变化，以及它如何在病理条件下改变。在神经工程学中，BMI 就像一棵树，它的分支伸向许多传统工程领域，但也将深深的根系延伸到神经假体之外的基础神经科学中。
