Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

当前针对运动皮层运动生成的建模研究，不再将神经活动解释为运动参数的函数（这类模型被称为表征模型（representational models）），而是将其视作在群体层面运作的动力学系统。尽管已有证据支持该框架，但现有文献中对表征模型的评估，以及其与动力学系统的整合研究仍存在不足。本研究基于表征性速度调谐对中心出发伸手任务开展模拟，结果表明，在神经活动与运动学信号之间引入可变延迟偏移，即可在神经群体层面生成旋转动力学现象——这一现象已在运动皮层中被观测到。不过，本研究开发了协方差匹配置换检验（CMPT）：该方法可为每个神经元独立地在不同任务条件间重新分配神经数据，同时保留神经元间的整体关联关系。分析结果显示，基于表征模型得到的旋转现象并非唯一依赖于底层的任务条件结构。与之相对，基于动力学模型或运动皮层数据得到的旋转现象则依赖于该关联关系，这一结果证明动力学模型能够更有效地解释运动皮层的神经活动。值得注意的是，通过搭建循环神经网络（recurrent neural network），本研究证实表征调谐特性与旋转动力学可同时涌现，这证明动力学系统能够复现此前关于表征调谐的研究结论。最后，结合运动皮层数据与协方差匹配置换检验（CMPT），本研究表明，基于少量神经元或任务条件得到的结果需谨慎解读，这一结论可为未来的实验设计提供参考。综上，本研究结果进一步支持了这一观点：表征模型无法充分解释单个神经元以及群体层面神经活动的复杂特征。