Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex.
Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex.
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DOI：
10.7554/elife.77690
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发表时间：
2023-05-11
期刊：
影响因子：
7.7
通讯作者：
Brunel N
中科院分区：
文献类型：
--
作者：
Bachschmid-Romano L;Hatsopoulos NG;Brunel N
初级运动皮层已被证明是协调运动的准备和执行，通过计算近似正交的子空间。潜在的网络机制，以及外部和循环连接所扮演的角色，是需要回答的核心开放问题，以了解运动控制的神经基质。我们开发了一个递归神经网络模型，概括了猕猴的初级运动皮层在指示延迟到达任务期间记录的神经元活动的时间演变。特别是，它再现了所观察到的神经活动和运动方向之间的协变的动态模式。我们探讨的假设，观察到的动态出现从突触连接结构，取决于在准备和运动相关的时期的神经元的首选方向，我们约束的强度突触连接和外部输入参数的数据。虽然该模型可以再现前馈和递归连接的多种组合的神经活动，但需要最小外部输入的解决方案是其中观察到的协方差模式由运动准备期间的外部输入形成，而它们在运动执行期间由强方向特定的递归连接主导。我们的模型还表明，单神经元调谐特性随时间变化的方式可以解释预备和运动相关子空间的正交性水平。
The primary motor cortex has been shown to coordinate movement preparation and execution through computations in approximately orthogonal subspaces. The underlying network mechanisms, and the roles played by external and recurrent connectivity, are central open questions that need to be answered to understand the neural substrates of motor control. We develop a recurrent neural network model that recapitulates the temporal evolution of neuronal activity recorded from the primary motor cortex of a macaque monkey during an instructed delayed-reach task. In particular, it reproduces the observed dynamic patterns of covariation between neural activity and the direction of motion. We explore the hypothesis that the observed dynamics emerges from a synaptic connectivity structure that depends on the preferred directions of neurons in both preparatory and movement-related epochs, and we constrain the strength of both synaptic connectivity and external input parameters from data. While the model can reproduce neural activity for multiple combinations of the feedforward and recurrent connections, the solution that requires minimum external inputs is one where the observed patterns of covariance are shaped by external inputs during movement preparation, while they are dominated by strong direction-specific recurrent connectivity during movement execution. Our model also demonstrates that the way in which single-neuron tuning properties change over time can explain the level of orthogonality of preparatory and movement-related subspaces.