[论文解读] The encoding of proprioceptive inputs in the brain: knowns and unknowns from a robotic perspective

Matej Hoffmann, Nada Bednárová|arXiv (Cornell University)|Jul 20, 2016

Neural dynamics and brain function参考文献 28被引用 6

一句话总结

本文利用iCub人形机器人的机器人模型，研究了来自肌梭的本体感觉信号在大脑中的编码方式。通过应用自组织映射（SOM）模拟从关节角度获得的姿势神经表征，发现SOM能够学习到姿势选择性的感受野，但在群体编码和非线性调谐曲线方面存在困难，凸显了其在建模生物上合理的本体感觉编码方面的局限性。

ABSTRACT

Somatosensory inputs can be grossly divided into tactile (or cutaneous) and proprioceptive -- the former conveying information about skin stimulation, the latter about limb position and movement. The principal proprioceptors are constituted by muscle spindles, which deliver information about muscle length and speed. In primates, this information is relayed to the primary somatosensory cortex and eventually the posterior parietal cortex, where integrated information about body posture (postural schema) is presumably available. However, coming from robotics and seeking a biologically motivated model that could be used in a humanoid robot, we faced a number of difficulties. First, it is not clear what neurons in the ascending pathway and primary somatosensory cortex code. To an engineer, joint angles would seem the most useful variables. However, the lengths of individual muscles have nonlinear relationships with the angles at joints. Kim et al. (Neuron, 2015) found different types of proprioceptive neurons in the primary somatosensory cortex -- sensitive to movement of single or multiple joints or to static postures. Second, there are indications that the somatotopic arrangement ("the homunculus") of these brain areas is to a significant extent learned. However, the mechanisms behind this developmental process are unclear. We will report first results from modeling of this process using data obtained from body babbling in the iCub humanoid robot and feeding them into a Self-Organizing Map (SOM). Our results reveal that the SOM algorithm is only suited to develop receptive fields of the posture-selective type. Furthermore, the SOM algorithm has intrinsic difficulties when combined with population code on its input and in particular with nonlinear tuning curves (sigmoids or Gaussians).

研究动机与目标

• 开发一种用于人形机器人的人体生物启发式本体感觉编码模型。
• 通过机器人身体自言自语（body babbling）方法，研究从感觉输入中涌现出的肢体姿势神经表征。
• 评估自组织映射（SOM）在建模体感皮层体感图中的适用性。
• 识别将SOM应用于非线性、群体编码的本体感觉输入时的局限性。
• 探讨发育机制在从感觉运动经验中塑造体感图组织中的作用。

提出的方法

• 使用iCub人形机器人通过随机身体运动（‘身体自言自语’）生成本体感觉数据。
• 收集关节角度数据作为肌梭传入神经的代理，模拟传入大脑的感觉输入。
• 应用自组织映射（SOM）算法，从关节角度学习姿势构型的拓扑表征。
• 通过在输入SOM前使用S形或高斯调谐曲线对关节角度进行群体编码，探索群体编码的影响。
• 通过限制感受野大小（MRF-SOM）对SOM进行修改，以更好地反映皮层感受野组织。
• 评估输入预处理（群体编码）对SOM学习性能和表征保真度的影响。

实验结果

研究问题

• RQ1 自组织映射能否从本体感觉输入中学习到生物上合理的肢体姿势表征？
• RQ2 关节角度的群体编码如何影响SOM的学习效果和拓扑保持？
• RQ3 为何标准SOM无法表征初级体感皮层中观察到的位置缩放（强度编码）神经元反应？
• RQ4 身体自言自语数据在多大程度上能诱导出体感图发育所需的关联结构？
• RQ5 SOM算法需要进行何种修改，才能反映后顶叶皮层中感受野层级扩展的特征？

主要发现

• SOM算法能够成功学习频繁姿势或姿势协同的表征，形成姿势选择性的感受野。
• SOM无法保持位置缩放特性——即神经元放电随关节角度单调增加——这是由于向量量化和拓扑保持的固有局限性所致。
• 标准SOM中输入与输出神经元之间的全连接结构导致拓扑失真，因为二维输出无法充分表征10个自由度的高维输入流形。
• 使用非线性调谐曲线（如高斯或S形）进行群体编码会破坏SOM的学习过程，因非单调性和不可逆性导致不一致的变换。
• MRF-SOM通过限制感受野大小，改善了表征保真度，模拟了皮层层级中感受野的层级扩展。
• 直接使用机器人中的关节角度输入比群体编码输入表现更优，表明大脑可能避免此类编码方式，或采用其他学习规则。