A Vector-Based Computational Model of Multimodal Insect Learning Walks.

ABSTRACT: Navigation is crucial for animal survival, and despite their small brains, insects are impressive at it. For example, desert ants acquire environmental information by learning to walk before foraging, enabling them to return home accurately over long distances. These learning walks involve multimodal sensory experiences and induce neuroplastic changes in the Central Complex (CX) and the Mushroom Body (MB) of ants' brains, making them a key topic in behavioural science, neuroscience, and computational modelling. To address unresolved questions in how ants integrate sensory cues and adapt navigation strategies, we propose a computational model that achieves multisensory integration during learning walks. Central to this model is a novel Learning Vector mechanism that dynamically combines vis

SUBMITTER: Xiang Z 

PROVIDER: S-EPMC12650027 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature