# Bio-Inspired Hashing for Unsupervised Similarity Search

arXiv: https://arxiv.org/abs/2001.04907 · HTML: https://mlsanity.com/paper/2001.04907/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts](https://mlsanity.com/paper/2510.08396/) (2025)
- [Can a Fruit Fly Learn Word Embeddings?](https://mlsanity.com/paper/2101.06887/) (2021)
- [Algorithmic insights on continual learning from fruit flies](https://mlsanity.com/paper/2107.07617/) (2021)
- [Sparse Distributed Memory is a Continual Learner](https://mlsanity.com/paper/2303.11934/) (2023)
- [Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and Reduced Training Time in Pre-trained Model-based Continual Representation Learning](https://mlsanity.com/paper/2510.16877/) (2025)
- [Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement](https://mlsanity.com/paper/2112.07157/) (2021)
- [DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression](https://mlsanity.com/paper/2202.10584/) (2022)
- [Hebbian Continual Representation Learning](https://mlsanity.com/paper/2207.04874/) (2022)
- [Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval](https://mlsanity.com/paper/2403.13747/) (2024)
- [On Design Choices in Similarity-Preserving Sparse Randomized Embeddings](https://mlsanity.com/paper/2501.14741/) (2024)
- [Comply: Learning Sentences with Complex Weights inspired by Fruit Fly Olfaction](https://mlsanity.com/paper/2502.01706/) (2025)

## Abstract (arXiv)

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.