Filippi Giulio, Knight James, Philippides Andrew, Graham Paul
School of Life Sciences, University of Sussex, Brighton, United Kingdom.
Department of Informatics, University of Sussex, Brighton, United Kingdom.
PLoS Comput Biol. 2025 Jun 23;21(6):e1012670. doi: 10.1371/journal.pcbi.1012670. eCollection 2025 Jun.
Many insects use memories of their visual environment to adaptively drive spatial behaviours. In ants, visual memories are fundamental for navigation, whereby foragers follow long visually guided routes to foraging sites and return to the location of their nest. Whilst we understand the basic visual pathway to the memory centres (Optic Lobes to Mushroom Bodies) involved in the storage of visual information, it is still largely unknown what type of representation of visual scenes underpins view-based navigation in ants. Several experimental studies have suggested ants use "higher-order" visual information - that is features extracted across the whole extent of a visual scene - which raises the question as to how these features might be computed. One such experimental study showed that ants can use the proportion of a shape experienced left of their visual centre to learn and recapitulate a route, a feature referred to as "fractional position of mass" (FPM). In this work, we use a simple model constrained by the known neuroanatomy and information processing properties of the Mushroom Bodies to explore whether the apparent use of the FPM could be a resulting factor of the bilateral organisation of the insect brain, all the whilst assuming a simple "retinotopic" view representation. We demonstrate that such bilaterally organised memory models can implicitly encode the FPM learned during training. We find that balancing the "quality" of the memory match across left and right hemispheres allows a trained model to retrieve the FPM defined direction, even when the model is tested with novel shapes, as demonstrated by ants. The result is shown to be largely independent of model parameter values, therefore suggesting that some aspects of higher-order processing of a visual scene may be emergent from the structure of the neural circuits, rather than computed in discrete processing modules.
许多昆虫利用对视觉环境的记忆来适应性地驱动空间行为。在蚂蚁中，视觉记忆对于导航至关重要，觅食者通过漫长的视觉引导路线前往觅食地点并返回巢穴位置。虽然我们了解参与视觉信息存储的通往记忆中心（从视叶到蘑菇体）的基本视觉通路，但在很大程度上仍不清楚何种视觉场景表征支撑着蚂蚁基于视图的导航。几项实验研究表明，蚂蚁使用“高阶”视觉信息——即从整个视觉场景中提取的特征——这就引发了这些特征如何被计算的问题。一项这样的实验研究表明，蚂蚁可以利用视觉中心左侧所经历形状的比例来学习和重现路线，这一特征被称为“质量分数位置”（FPM）。在这项工作中，我们使用一个受蘑菇体已知神经解剖结构和信息处理特性约束的简单模型，来探索FPM的明显使用是否可能是昆虫大脑双侧组织的结果，同时假设一种简单的“视网膜拓扑”视图表征。我们证明，这种双侧组织的记忆模型可以隐式编码训练期间学到的FPM。我们发现，平衡左右半球记忆匹配的“质量”能使经过训练的模型检索到由FPM定义的方向，即使该模型用新形状进行测试时也是如此，就像蚂蚁所展示的那样。结果表明在很大程度上与模型参数值无关，因此表明视觉场景高阶处理的某些方面可能源自神经回路的结构，而非在离散处理模块中进行计算。
