The fruit fly Drosophila's olfactory circuit has inspired a new locality
sensitive hashing (LSH) algorithm, FlyHash. In contrast with classical LSH
algorithms that produce low dimensional hash codes, FlyHash produces sparse
high-dimensional hash codes and has also been shown to have superior empirical
performance compared to classical LSH algorithms in similarity search. However,
FlyHash uses random projections and cannot learn from data. Building on
inspiration from FlyHash and the ubiquity of sparse expansive representations
in neurobiology, our work proposes a novel hashing algorithm BioHash that
produces sparse high dimensional hash codes in a data-driven manner. We show
that BioHash outperforms previously published benchmarks for various hashing
methods. Since our learning algorithm is based on a local and biologically
plausible synaptic plasticity rule, our work provides evidence for the proposal
that LSH might be a computational reason for the abundance of sparse expansive
motifs in a variety of biological systems. We also propose a convolutional
variant BioConvHash that further improves performance. From the perspective of
computer science, BioHash and BioConvHash are fast, scalable and yield
compressed binary representations that are useful for similarity search.

基于果蝇嗅觉电路启发的两种哈希算法：FlyHash 和 BioHash，分别产生稀疏的高维哈希码，在数据驱动的过程中被证明是高效的相似性搜索方法，提出了结合生物学机制的 LSH 假设，是计算机科学领域的重要研究成果。