Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA.
Cereb Cortex. 2019 Apr 1;29(4):1619-1633. doi: 10.1093/cercor/bhy060.
A complex action can be described as the composition of a set of elementary movements. While both kinematic and dynamic elements have been proposed to compose complex actions, the structure of movement decomposition and its neural representation remain unknown. Here, we examined movement decomposition by modeling the temporal dynamics of neural populations in the primary motor cortex of macaque monkeys performing forelimb reaching movements. Using a hidden Markov model, we found that global transitions in the neural population activity are associated with a consistent segmentation of the behavioral output into acceleration and deceleration epochs with directional selectivity. Single cells exhibited modulation of firing rates between the kinematic epochs, with abrupt changes in spiking activity timed with the identified transitions. These results reveal distinct encoding of acceleration and deceleration phases at the level of M1, and point to a specific pattern of movement decomposition that arises from the underlying neural activity. A similar approach can be used to probe the structure of movement decomposition in different brain regions, possibly controlling different temporal scales, to reveal the hierarchical structure of movement composition.
一个复杂的动作可以被描述为一组基本动作的组合。虽然运动学和动力学元素都被提议来组成复杂的动作,但运动分解的结构及其神经表示仍然未知。在这里,我们通过对猕猴初级运动皮层中神经群体的时间动态进行建模来检查运动分解。使用隐马尔可夫模型,我们发现神经群体活动的全局跃迁与行为输出的一致分段有关,这些分段具有方向选择性。单个细胞在运动学时期之间表现出放电率的调制,其尖峰活动的突然变化与所确定的转变同步。这些结果揭示了 M1 水平上加速和减速阶段的明显编码,并指出了一种特定的运动分解模式,这种模式源于基础神经活动。类似的方法可以用于探测不同脑区运动分解的结构,可能控制不同的时间尺度,以揭示运动组成的层次结构。