Department of Physiology and Pharmacology, Western University, London, Canada.
Brain and Mind Institute, Western University, London, Canada.
PLoS Comput Biol. 2020 Dec 2;16(12):e1008303. doi: 10.1371/journal.pcbi.1008303. eCollection 2020 Dec.
Our ability to manipulate objects relies on tactile inputs from first-order tactile neurons that innervate the glabrous skin of the hand. The distal axon of these neurons branches in the skin and innervates many mechanoreceptors, yielding spatially-complex receptive fields. Here we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. In particular, our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the human tactile processing pathway and how they may be critical for supporting hand function.
我们操纵物体的能力依赖于第一级触觉神经元的触觉输入，这些神经元支配着手部的无毛发皮肤。这些神经元的远轴突在皮肤中分支，并支配着许多机械感受器，产生空间复杂的感受野。在这里，我们表明，第一级神经元群体的复杂信号的突触整合可能是人类能够准确（<3°）和快速处理指尖上移动的边缘的能力的基础。我们首先推导出符合并预测对移动边缘的反应的人类第一级触觉神经元的尖峰模型，具有很高的准确性。然后，我们使用模型神经元来模拟支配指尖的外围神经元群体。我们训练执行神经元群体活动的突触整合的分类器，并表明第一级神经元的突触整合可以以很高的灵敏度和速度处理边缘方向。特别是，我们的模型表明，在短时间尺度内整合快速衰减（AMPA 样）的突触输入对于区分精细方向至关重要，而整合缓慢衰减（NMDA 样）的突触输入支持较粗方向的区分，并在较长时间尺度上保持鲁棒性。总之，我们的结果为人类触觉处理途径的最早阶段发生的计算提供了新的见解，以及它们如何对支持手部功能至关重要。
