2025 Fly Connectome Research Competition - 2026–2027 Connectomics Research Competition

In 2025, we hosted the first International Synapse-Level Connectomics Competition, where participants explored neuronal morphology and connectivity across the Drosophila optic lobe synapse-level connectome (Nern et al., 2024, Nature) using cutting-edge connectomics tools such as Neuroglancer and neuPrint.

Although the scientific focus differs from this year’s competition, many of the educational materials—particularly those related to Track 1—remain highly relevant. If you are interested in learning more about connectomics, we encourage you to explore the resources from last year’s competition:

Competition website: https://flycomp2025.my.canva.site/

Congratulations to Our 2025 Winners¶

We would like to once again congratulate all participants and award recipients!

High School Category¶

Winner

🥇 Pakawat Poldetch — Ruamrudee International School, Thailand (H1 neuron)

Runners-Up

🥈 Sanviti Amarnath — Branham High School, USA (Tm3 neuron)
🥈 Silvia Zeng — Malvern College, United Kingdom (T4b neuron)

Honorable Mention – Science Communication Award

🥇 Rita Li — Agincourt Collegiate Institute, Canada (I-LNv neuron)

Undergraduate Category¶

Winners (Tie)

🥇 Mohamed Hesham Naas — University of Tripoli, Libya (Mi19 neuron)
🥇 Renoy Antony Olivera — Father Muller College of Allied Health Sciences, India (OA neurons)

Runner-Up

🥈 Zaina Chowdhury — University of Toronto, Canada (Mi1 neuron)

Honorable Mention – Science Communication Award

🥇 Sajeda Khalid Kareem — University of Tripoli, Libya (T5d neuron)

Beyond Research Competitions¶

One of the goals of the competition is to encourage participants to undertake research that can contribute to the scientific community.

Following the 2025 competition, Renoy Antony Olivera further developed his competition project into a peer-reviewed research article:

Olivera, R. A. (2026). Cross-Platform Neurotransmitter & Alias Ambiguity for OA-AL2b1 and OA-AL2b2 Neurons in Drosophila melanogaster. Neuroinformatics, 24(2), 25. https://link.springer.com/article/10.1007/s12021-026-09783-4