One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. However, no comprehensive model exists that links all steps of processing from vision to action. We hypothesized that a recurrent neural network mimicking the modular structure of the anatomical circuit and trained to use visual features of objects to generate the required muscle dynamics used by primates to grasp objects would give insight into the computations of the grasping circuit. Internal activity of modular networks trained with these constraints strongly resembled neural activity recorded from the grasping circuit during grasping and paralleled the similarities between brain regions. Network activity during the different phases of the task could be explained by linear dynamics for maintaining a distributed movement plan across the network in the absence of visual stimulus and then generating the required muscle kinematics based on these initial conditions in a module-specific way. These modular models also outperformed alternative models at explaining neural data, despite the absence of neural data during training, suggesting that the inputs, outputs, and architectural constraints imposed were sufficient for recapitulating processing in the grasping circuit. Finally, targeted lesioning of modules produced deficits similar to those observed in lesion studies of the grasping circuit, providing a potential model for how brain regions may coordinate during the visually guided grasping of objects.

我们与世界互动的主要方式之一就是动手。在猕猴中，跨越前顶壁内区，腹侧前运动皮层的手区域和初级运动皮层的回路对于将视觉信息转换为抓紧动作是必要的。但是，不存在将从愿景到行动的所有处理步骤链接在一起的综合模型。我们假设模仿神经回路的模块化结构并受过训练以使用物体的视觉特征来生成灵长类动物用来抓握物体的所需肌肉动力学的递归神经网络，将使人们更加了解抓握回路的计算。受这些约束训练的模块化网络的内部活动与抓取过程中从抓取电路记录的神经活动非常相似，并使大脑区域之间的相似性平行。任务的不同阶段中的网络活动可以用线性动力学来解释，该线性动力学用于在没有视觉刺激的情况下在整个网络上维持分布的运动计划，然后根据这些初始条件以模块特定的方式生成所需的肌肉运动学。尽管在训练期间没有神经数据，但是这些模块化模型在解释神经数据时也胜过其他模型，这表明所施加的输入，输出和体系结构约束足以概括控制电路中的处理。最后，