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Paradigm Shift in Sensorimotor Control Research and Brain Machine Interface Control: The Influence of Context on Sensorimotor Representations

机译:Sensorimotor控制研究和脑机接口控制的范式转换:语境对传感器表示的影响

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摘要

Neural activity in the primary motor cortex (M1) is known to correlate with movement related variables including kinematics and dynamics. Our recent work, which we believe is part of a paradigm shift in sensorimotor research, has shown that in addition to these movement related variables, activity in M1 and the primary somatosensory cortex (S1) are also modulated by context, such as value, during both active movement and movement observation. Here we expand on the investigation of reward modulation in M1, showing that reward level changes the neural tuning function of M1 units to both kinematic as well as dynamic related variables. In addition, we show that this reward-modulated activity is present during brain machine interface (BMI) control. We suggest that by taking into account these context dependencies of M1 modulation, we can produce more robust BMIs. Toward this goal, we demonstrate that we can classify reward expectation from M1 on a movement-by-movement basis under BMI control and use this to gate multiple linear BMI decoders toward improved offline performance. These findings demonstrate that it is possible and meaningful to design a more accurate BMI decoder that takes reward and context into consideration. Our next step in this development will be to incorporate this gating system, or a continuous variant of it, into online BMI performance.
机译:已知主电动机皮质(M1)中的神经活动与包括运动学和动态的运动相关变量相关联。我们最近的工作是传感器研究中的范式转变的一部分,表明除了这些运动相关的变量之外,M1和初级躯体传感皮层的活性还通过上下文调制,例如价值,期间主动运动和运动观察。在这里,我们扩展了M1中奖励调制的调查,显示奖励水平将M1单元的神经调整功能改变为运动学以及动态相关变量。此外,我们表明,脑机接口(BMI)控制期间存在此奖励调制活动。我们建议通过考虑到M1调制的这些上下文依赖性,我们可以产生更强大的BMIS。对于实现这一目标,我们证明我们可以在BMI控制下对M1进行奖励期望,并在BMI控制下对移动的基础进行分类,并将其用于拓宽多个线性BMI解码器,以改善离线性能。这些调查结果表明,设计更准确的BMI解码器,以考虑奖励和上下文,可以和有意义。我们在此开发的下一步将是将此门控系统或其连续变种纳入在线BMI性能。

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