Model-based neural decoding of reaching movements: A maximum likelihood approach
Model-based neural decoding of reaching movements: A maximum likelihood approach
复制标题
DOI：
10.1109/tbme.2004.826675
复制
发表时间：
2004-06-01
影响因子：
4.6
通讯作者：
Meng, TH
中科院分区：
文献类型：
--
作者：
Kemere, C;Shenoy, KV;Meng, TH
提出了一种新的范式，用于从单个神经元的集合的信号解码到达运动。这种新方法不仅为该任务提供了新的理论基础，而且还导致重建的手轨迹的误差显着减少。通过使用一个模型的运动作为解码系统的基础，我们表明，所需的神经元的数量重建的点到点达到运动的轨迹在两个维度上可以减半。此外，使用所提出的框架，其他形式的神经信息，特别是神经“计划”活动，可以被集成到轨迹解码过程中。解码范例进行了测试，在模拟实验中使用的数据库收集的中心外达到和相应的神经数据生成的合成模型。
A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models.