Neurobiology Laboratory, Deutsches Primatenzentrum GmbH, 37077 Goettingen, Germany.
Brain and Mind Institute, Western University, London, ON N6A 5B7, Canada.
Proc Natl Acad Sci U S A. 2020 Dec 15;117(50):32124-32135. doi: 10.1073/pnas.2005087117. Epub 2020 Nov 30.
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是使用双手。在猕猴中，从前内顶叶区、腹侧前运动皮层的手部区域以及初级运动皮层跨越的回路对于将视觉信息转化为抓握运动是必要的。然而，目前还没有一个综合的模型将从视觉到动作的所有处理步骤联系起来。我们假设，一个模仿解剖学回路模块化结构的递归神经网络，经过训练可以使用物体的视觉特征来生成灵长类动物抓握物体所需的肌肉动力学，这将有助于深入了解抓握回路的计算。用这些约束条件训练的模块化网络的内部活动强烈地类似于抓握回路在抓握过程中记录的神经活动，并且与大脑区域之间的相似性相平行。在任务的不同阶段，网络活动可以用线性动力学来解释，这种动力学可以在没有视觉刺激的情况下，在网络中保持分布式运动计划，然后根据这些初始条件以特定于模块的方式生成所需的肌肉运动学。尽管在训练过程中没有神经数据，但这些模块化模型在解释神经数据方面的表现也优于替代模型，这表明输入、输出和所施加的架构约束足以再现抓握回路中的处理过程。最后，模块的靶向损毁产生的缺陷类似于抓握回路损毁研究中观察到的缺陷，为大脑区域在视觉引导的物体抓握过程中如何协调提供了一个潜在的模型。
