Mixture of trajectory models for neural decoding of goal-directed movements.
Mixture of trajectory models for neural decoding of goal-directed movements.
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DOI：
10.1152/jn.00482.2006
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发表时间：
2007-05
影响因子：
2.5
通讯作者：
Byron M. Yu;C. Kemere;G. Santhanam;A. Afshar;Stephen Ryu;T. H. Meng;M. Sahani;K. Shenoy
中科院分区：
文献类型：
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作者：
Byron M. Yu;C. Kemere;G. Santhanam;A. Afshar;Stephen Ryu;T. H. Meng;M. Sahani;K. Shenoy
概率解码技术已经成功地用于从神经数据推断随时间演化的物理状态，例如手臂轨迹或觅食大鼠的路径。这种解码器的一个重要元素是轨迹模型，表达有关运动统计特性的知识。不幸的是，1）准确描述运动统计和2）允许具有相对低的计算需求的解码器的轨迹模型可能难以构建。简单的模型计算成本低，但往往不准确。更复杂的模型可以获得准确性，但以更高的计算成本为代价，阻碍了它们用于实时解码。在这里，我们提出了一种新的通用方法来定义轨迹模型，同时满足这两个要求。其核心思想是将联合收割机简单的轨迹模型组合在一个概率混合轨迹模型（MTM）中，每个模型在有限的运动范围内都是精确的。我们证明了该方法的实用性，通过使用MTM解码器来推断目标导向的达到多个离散目标的运动，从记录在猴子运动和前运动皮层的多电极神经数据。与使用更简单的轨迹模型的解码器相比，MTM解码器在两只猴子中使用98（99）个单元将解码错误减少了38%（48%），而无需增加运行时间。当可用时，关于即将到来的可达目标的身份的先验信息可以以原则性的方式并入，从而进一步将解码错误减少20（11）%。总的来说，这些进步应该允许假肢光标或肢体更准确地移动到预期的到达目标。
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.