Yu Byron M, Kemere Caleb, Santhanam Gopal, Afshar Afsheen, Ryu Stephen I, Meng Teresa H, Sahani Maneesh, Shenoy Krishna V
Department of Electrical Engineering, Stanford University, CA 94305-4075, USA.
J Neurophysiol. 2007 May;97(5):3763-80. doi: 10.1152/jn.00482.2006. Epub 2007 Feb 28.
Probabilistic decoding techniques have been used successfully to infer time-evolving physical state, such as arm trajectory or the path of a foraging rat, from neural data. A vital element of such decoders is the trajectory model, expressing knowledge about the statistical regularities of the movements. Unfortunately, trajectory models that both 1) accurately describe the movement statistics and 2) admit decoders with relatively low computational demands can be hard to construct. Simple models are computationally inexpensive, but often inaccurate. More complex models may gain accuracy, but at the expense of higher computational cost, hindering their use for real-time decoding. Here, we present a new general approach to defining trajectory models that simultaneously meets both requirements. The core idea is to combine simple trajectory models, each accurate within a limited regime of movement, in a probabilistic mixture of trajectory models (MTM). We demonstrate the utility of the approach by using an MTM decoder to infer goal-directed reaching movements to multiple discrete goals from multi-electrode neural data recorded in monkey motor and premotor cortex. Compared with decoders using simpler trajectory models, the MTM decoder reduced the decoding error by 38 (48) percent in two monkeys using 98 (99) units, without a necessary increase in running time. When available, prior information about the identity of the upcoming reach goal can be incorporated in a principled way, further reducing the decoding error by 20 (11) percent. Taken together, these advances should allow prosthetic cursors or limbs to be moved more accurately toward intended reach goals.
概率解码技术已成功用于从神经数据中推断随时间演变的物理状态，如手臂轨迹或觅食大鼠的路径。此类解码器的一个关键要素是轨迹模型，它表达了有关运动统计规律的知识。不幸的是，很难构建同时满足以下两个条件的轨迹模型：1）准确描述运动统计规律；2）允许具有相对较低计算需求的解码器。简单模型计算成本低，但往往不准确。更复杂的模型可能会提高准确性，但以更高的计算成本为代价，这阻碍了它们在实时解码中的应用。在此，我们提出了一种定义轨迹模型的新通用方法，该方法同时满足这两个要求。核心思想是在轨迹模型的概率混合（MTM）中组合简单轨迹模型，每个模型在有限的运动范围内是准确的。我们通过使用MTM解码器从猴子运动和运动前皮层记录的多电极神经数据中推断对多个离散目标的目标导向伸手运动，证明了该方法的实用性。与使用更简单轨迹模型的解码器相比，MTM解码器在两只猴子中使用98（99）个单元将解码误差降低了38（48）%，而运行时间没有必然增加。如果有即将到来的伸手目标的身份的先验信息，可以以一种有原则的方式纳入，进一步将解码误差降低20（11）%。综上所述，这些进展应能使假肢光标或肢体更准确地朝着预期的伸手目标移动。
