# Rapid online learning and robust recall in a neuromorphic olfactory circuit

arXiv: https://arxiv.org/abs/1906.07067 · HTML: https://mlsanity.com/paper/1906.07067/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 18 of 18)

- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks](https://mlsanity.com/paper/2106.11908/) (2021)
- [HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds](https://mlsanity.com/paper/2308.10373/) (2023)
- [BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks](https://mlsanity.com/paper/2110.14092/) (2021)
- [Multimodal Data Storage and Retrieval for Embodied AI: A Survey](https://mlsanity.com/paper/2508.13901/) (2025)
- [Limitations in odour recognition and generalisation in a neuromorphic olfactory circuit](https://mlsanity.com/paper/2309.11555/) (2023)
- [Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence](https://mlsanity.com/paper/2506.00398/) (2025)
- [The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture Recognition](https://mlsanity.com/paper/2206.01910/) (2022)
- [Unsupervised Attention-Based Multi-Source Domain Adaptation Framework for Drift Compensation in Electronic Nose Systems](https://mlsanity.com/paper/2409.13167/) (2024)
- [Heterogeneous quantization regularizes spiking neural network activity](https://mlsanity.com/paper/2409.18396/) (2024)
- [Staying Alive: Online Neural Network Maintenance and Systemic Drift](https://mlsanity.com/paper/2503.17681/) (2025)
- [CSDformer: A Conversion Method for Fully Spike-Driven Transformer](https://mlsanity.com/paper/2509.17461/) (2025)
- [ASecond-Order SpikingSSM for Wearables](https://mlsanity.com/paper/2510.14386/) (2025)
- [Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network](https://mlsanity.com/paper/2511.01553/) (2025)
- [A Universal and Robust Framework for Multiple Gas Recognition Based-on Spherical Normalization-Coupled Mahalanobis Algorithm](https://mlsanity.com/paper/2512.22792/) (2025)
- [ArrowFlow: Hierarchical Machine Learning in the Space of Permutations](https://mlsanity.com/paper/2604.04087/) (2026)
- [Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization](https://mlsanity.com/paper/2606.31695/) (2026)

## Abstract (arXiv)

We present a neural algorithm for the rapid online learning and identification of odorant samples under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system. As with biological olfaction, the spike timing-based algorithm utilizes distributed, event-driven computations and rapid (one-shot) online learning. Spike timing-dependent plasticity rules operate iteratively over sequential gamma-frequency packets to construct odor representations from the activity of chemosensor arrays mounted in a wind tunnel. Learned odorants then are reliably identified despite strong destructive interference. Noise resistance is further enhanced by neuromodulation and contextual priming. Lifelong learning capabilities are enabled by adult neurogenesis. The algorithm is applicable to any signal identification problem in which high-dimensional signals are embedded in unknown backgrounds.