University of Bologna, Department of Biomedical and Neuromotor Sciences, Bologna, Italy.
University of Bologna, Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", Cesena Campus, Cesena, Italy.
Neural Netw. 2022 Jul;151:276-294. doi: 10.1016/j.neunet.2022.03.044. Epub 2022 Apr 5.
Despite the well-recognized role of the posterior parietal cortex (PPC) in processing sensory information to guide action, the differential encoding properties of this dynamic processing, as operated by different PPC brain areas, are scarcely known. Within the monkey's PPC, the superior parietal lobule hosts areas V6A, PEc, and PE included in the dorso-medial visual stream that is specialized in planning and guiding reaching movements. Here, a Convolutional Neural Network (CNN) approach is used to investigate how the information is processed in these areas. We trained two macaque monkeys to perform a delayed reaching task towards 9 positions (distributed on 3 different depth and direction levels) in the 3D peripersonal space. The activity of single cells was recorded from V6A, PEc, PE and fed to convolutional neural networks that were designed and trained to exploit the temporal structure of neuronal activation patterns, to decode the target positions reached by the monkey. Bayesian Optimization was used to define the main CNN hyper-parameters. In addition to discrete positions in space, we used the same network architecture to decode plausible reaching trajectories. We found that data from the most caudal V6A and PEc areas outperformed PE area in the spatial position decoding. In all areas, decoding accuracies started to increase at the time the target to reach was instructed to the monkey, and reached a plateau at movement onset. The results support a dynamic encoding of the different phases and properties of the reaching movement differentially distributed over a network of interconnected areas. This study highlights the usefulness of neurons' firing rate decoding via CNNs to improve our understanding of how sensorimotor information is encoded in PPC to perform reaching movements. The obtained results may have implications in the perspective of novel neuroprosthetic devices based on the decoding of these rich signals for faithfully carrying out patient's intentions.
尽管后顶叶皮层（PPC）在处理感觉信息以指导动作方面的作用得到了广泛认可，但不同 PPC 脑区在操作过程中这种动态处理的差异编码特性却鲜为人知。在猴子的 PPC 中，上顶叶包含了 V6A、PEc 和 PE 等区域，这些区域属于背侧视觉流，专门用于规划和指导伸手动作。在这里，我们使用卷积神经网络（CNN）方法来研究这些区域中的信息是如何被处理的。我们训练了两只猕猴执行一个延迟伸手任务，目标是在 3D 近体空间的 9 个位置（分布在 3 个不同的深度和方向水平上）。我们从 V6A、PEc 和 PE 中记录单个细胞的活动，并将其输入到卷积神经网络中，这些网络是专门设计和训练来利用神经元激活模式的时间结构，以解码猴子到达的目标位置。贝叶斯优化用于定义主要的 CNN 超参数。除了空间中的离散位置外，我们还使用相同的网络架构来解码合理的伸手轨迹。我们发现，来自最尾侧 V6A 和 PEc 区域的数据在空间位置解码方面优于 PE 区域。在所有区域中，解码精度在向猴子指示目标位置时开始增加，并在运动开始时达到一个平台期。结果支持了不同相位和伸手运动特性的动态编码，这些编码在一个相互连接的区域网络中差异分布。这项研究强调了通过 CNN 对神经元放电率进行解码，以提高我们对 PPC 中如何编码感觉运动信息以执行伸手运动的理解的有用性。所获得的结果可能对基于这些丰富信号解码的新型神经假体设备具有重要意义，以便忠实地执行患者的意图。
