NeurIPS 2025poster0 citations

Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies

HaiYang Li, Liao Yu, Qiang Yu, Yunliang Zang

Abstract

Biological circuits have evolved to incorporate multiple modules that perform similar functions. In the fly olfactory circuit, both lateral inhibition (LI) and neuronal spike frequency adaptation (SFA) are thought to enhance pattern separation for odor learning. However, it remains unclear whether these mechanisms play redundant or distinct roles in this process. In this study, we present a computational model of the fly olfactory circuit to investigate odor discrimination under varying noise conditions that simulate complex environments. Our results show that LI primarily enhances odor discrimination in low‑ and medium‑noise scenarios, but this benefit diminishes and may reverse under higher‑noise conditions. In contrast, SFA consistently improves discrimination across all noise levels. LI is preferentially engaged in low‑ and medium‑noise environments, whereas SFA dominates in high‑noise settings. When combined, these two sparsification mechanisms enable optimal discrimination performance. This work demonstrates that seemingly redundant modules in biological circuits can, in fact, be essential for achieving optimal learning in complex contexts.

olfactory representationslateral inhibition;spike frequency adaptationpattern classification;spiking neural networks
BibTeX
@inproceedings{
li2025seemingly,
title={Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies},
author={HaiYang Li and Liao Yu and Qiang Yu and Yunliang Zang},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=d6WUTRJqP3}
}
Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies · NeurIPS 2025