Journal Article

Can Drosophila melanogaster tell who’s who?

Murali, Nihal, Schneider, Jonathan, Levine, Joel D., Taylor, Graham W.

2018

Overview

Drosophila melanogaster are known to live in a social but cryptic world of touch and odours, but the extent to which they can perceive and integrate static visual information is a hotly debated topic. Some researchers fixate on the limited resolution of D. melanogaster's optics, others on their seemingly identical appearance; yet there is evidence of individual recognition and surprising visual learning in flies. Here, we apply machine learning and show that individual D. melanogaster are visually distinct. We also use the striking similarity of Drosophila's visual system to current convolutional neural networks to theoretically investigate D. melanogaster's capacity for visual understanding. We find that, despite their limited optical resolution, D. melanogaster's neuronal architecture has the capability to extract and encode a rich feature set that allows flies to re-identify individual conspecifics with surprising accuracy. These experiments provide a proof of principle that Drosophila inhabit a much more complex visual world than previously appreciated.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Artificial neural networks / Behavior / Biology / Biology and Life Sciences / Conspecifics / Drosophila / Drosophila melanogaster / Engineering schools / Feature extraction / Information processing / Insects / Learning algorithms / Machine learning / Medicine and Health Sciences / Neural networks / Odors / Optics / Photoreceptors / Research and Analysis Methods / Semantics / Social Sciences / Visual discrimination learning / Visual perception / Visual system