What do navigating animals see? Reconstructing the visual histories of desert ants

Abstract

To understand how brains translate sensory input into behavioural outputs requires a fuller account of the sensory experience of individual animals in their natural settings. Previously we developed a visual rendering software capable of reconstructing the information that insect's compound eyes perceive (Millward et al, 2022). In this work, we extend this software pipeline to integrate data from animal tracking, habitat mapping, and compound eye modelling methods. The result is a new type of dataset that documents the natural foraging behaviour of desert ants (a model species for the study of animal navigation) within their natural habitat, combined with simulation tools to accurately reconstruct the visual experience of each forager as they learned to navigate their surroundings. This addresses a long-held need to create realistic training input for brain models complimented with verification tests in the same real ecologically relevant scenarios.

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BibTeX RIS

BibTeX

@misc{mangan2026what,
  title = {{What do navigating animals see? Reconstructing the visual histories of desert ants}},
  author = {M. Mangan and B. Millward and S. James and E. Baird},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.22.760929},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.22.760929v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - What do navigating animals see? Reconstructing the visual histories of desert ants
AU  - M. Mangan
AU  - B. Millward
AU  - S. James
AU  - E. Baird
PY  - 2026
DA  - 2026/10/22
PB  - bioRxiv
DO  - 10.64898/2026.10.22.760929
UR  - https://www.biorxiv.org/content/10.64898/2026.10.22.760929v1
AB  - To understand how brains translate sensory input into behavioural outputs requires a fuller account of the sensory experience of individual animals in their natural settings. Previously we developed a visual rendering software capable of reconstructing the information that insect's compound eyes perceive (Millward et al, 2022). In this work, we extend this software pipeline to integrate data from animal tracking, habitat mapping, and compound eye modelling methods. The result is a new type of dataset that documents the natural foraging behaviour of desert ants (a model species for the study of animal navigation) within their natural habitat, combined with simulation tools to accurately reconstruct the visual experience of each forager as they learned to navigate their surroundings. This addresses a long-held need to create realistic training input for brain models complimented with verification tests in the same real ecologically relevant scenarios.
ER  -

Abstract

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