Dept. of Computer Science, Western University, London, Ontario, Canada.
School of Medicine, Yale University, New Haven, Connecticut, United States of America.
PLoS One. 2018 Jun 14;13(6):e0199196. doi: 10.1371/journal.pone.0199196. eCollection 2018.
First-order tactile neurons have spatially complex receptive fields. Here we use machine-learning tools to show that such complexity arises for a wide range of training sets and network architectures. Moreover, we demonstrate that this complexity benefits network performance, especially on more difficult tasks and in the presence of noise. Our work suggests that spatially complex receptive fields are normatively good given the biological constraints of the tactile periphery.
第一级触觉神经元具有空间复杂的感受野。在这里，我们使用机器学习工具来表明，这种复杂性出现在广泛的训练集和网络架构中。此外，我们还证明了这种复杂性有利于网络性能，尤其是在更困难的任务和存在噪声的情况下。我们的工作表明，考虑到触觉外围的生物学限制，空间复杂的感受野是规范上的优势。
