果蝇嗅觉系统中看似冗余的两个机制协同提升复杂环境下的气味识别能力
Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies
- 构建果蝇嗅觉电路计算模型，模拟不同噪声环境下气味分辨
- 突触抑制在低中噪声下有效，适应性放电则在高噪声中持续提升分辨力
- 两者互补实现复杂环境最优学习，揭示冗余模块的协同价值
生物电路演化出多个功能相似的模块。在果蝇嗅觉回路中，侧向抑制（LI）和神经元脉冲频率适应（SFA）均被认为能增强气味学习中的模式分离。然而，这些机制在该过程中的作用是冗余还是独立尚不明确。本研究建立果蝇嗅觉回路的计算模型，探究在模拟复杂环境的不同噪声条件下气味区分性能。结果表明：在低、中等噪声下，LI显著提升区分度；但在高噪声下其效果减弱甚至逆转。相反，SFA在所有噪声水平下均持续改善区分表现。在低、中噪声环境中，主要依赖LI；而在高噪声下，SFA起主导作用。二者结合可实现最优区分性能。研究证明，生物电路中看似冗余的模块，在复杂情境下实为实现最优学习的关键。
原文摘要 · Abstract (English)
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.
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