Liu Yuan Sophie, Stevens Charles F, Sharpee Tatyana O
Computational Neurobiology Laboratory and Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
Proc Natl Acad Sci U S A. 2009 Sep 22;106(38):16499-504. doi: 10.1073/pnas.0908926106. Epub 2009 Sep 10.
Understanding how the nervous system achieves reliable performance using unreliable components is important for many disciplines of science and engineering, in part because it can suggest ways to lower the energetic cost of computing. In vision, retinal ganglion cells partition visual space into approximately circular regions termed receptive fields (RFs). Average RF shapes are such that they would provide maximal spatial resolution if they were centered on a perfect lattice. However, individual shapes have fine-scale irregularities. Here, we find that irregular RF shapes increase the spatial resolution in the presence of lattice irregularities from approximately 60% to approximately 92% of that possible for a perfect lattice. Optimization of RF boundaries around their fixed center positions reproduced experimental observations neuron-by-neuron. Our results suggest that lattice irregularities determine the shapes of retinal RFs and that similar algorithms can improve the performance of retinal prosthetics where substantial irregularities arise at their interface with neural tissue.
理解神经系统如何利用不可靠的组件实现可靠的性能，对许多科学和工程学科都很重要，部分原因在于它可以为降低计算的能量成本提供思路。在视觉方面，视网膜神经节细胞将视觉空间划分为近似圆形的区域，称为感受野（RFs）。平均感受野形状是这样的：如果它们以完美晶格为中心，就能提供最大空间分辨率。然而，单个形状存在精细尺度的不规则性。在这里，我们发现，在存在晶格不规则性的情况下，不规则的感受野形状将空间分辨率从完美晶格可能达到的约60%提高到了约92%。围绕其固定中心位置对感受野边界进行优化，逐神经元地再现了实验观察结果。我们的结果表明，晶格不规则性决定了视网膜感受野的形状，并且类似的算法可以改善视网膜假体的性能，因为在其与神经组织的界面处会出现大量不规则性。
