An artificial neural network explains how bats might use vision for navigation

Aya Goldshtein, Shimon Akrish, Raja Giryes, Yossi Yovel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Animals navigate using various sensory information to guide their movement. Miniature tracking devices now allow documenting animals’ routes with high accuracy. Despite this detailed description of animal movement, how animals translate sensory information to movement is poorly understood. Recent machine learning advances now allow addressing this question with unprecedented statistical learning tools. We harnessed this power to address visual-based navigation in fruit bats. We used machine learning and trained a convolutional neural network to navigate along a bat’s route using visual information that would have been available to the real bat, which we collected using a drone. We show that a simple feed-forward network can learn to guide the agent towards a goal based on sensory input, and can generalize its learning both in time and in space. Our analysis suggests how animals could potentially use visual input for navigation and which features might be useful for this purpose.

Original languageEnglish
Article number1325
JournalCommunications Biology
Volume5
Issue number1
DOIs
StatePublished - Dec 2022

Funding

FundersFunder number
ERC-Behavior-Island
European Research Council

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