Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
Science. 2017 Nov 10;358(6364):793-796. doi: 10.1126/science.aam9868.
Similarity search-for example, identifying similar images in a database or similar documents on the web-is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches. This perspective helps illuminate the logic supporting an important sensory function and provides a conceptually new algorithm for solving a fundamental computational problem.
相似性搜索——例如，在数据库中识别相似的图像或网络上相似的文档——是大规模信息检索系统面临的一个基本计算问题。我们发现，果蝇嗅觉回路使用一种计算机科学算法（称为局部敏感哈希）的变体来解决这个问题。果蝇回路将相似的神经活动模式分配给相似的气味，以便从一种气味中学习到的行为可以应用于体验到类似气味时。然而，果蝇的算法使用了三种与传统方法不同的计算策略。这些策略可以被转化，以提高计算相似性搜索的性能。这种观点有助于阐明支持重要感觉功能的逻辑，并为解决基本计算问题提供了一种全新的概念算法。
