---
title: "Eyewire"
chunk: 1/1
source: "https://en.wikipedia.org/wiki/Eyewire"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T04:14:24.211831+00:00"
instance: "kb-cron"
---

Eyewire is a citizen science game from Sebastian Seung's Lab at Princeton University. It is a human-based computation game that uses players to map retinal neurons. Eyewire launched on December 10, 2012. The game utilizes data generated by the Max Planck Institute for Medical Research. As of March 2025, Eyewire has had around 350,000 players and resulted in the tracing of 6,000 neurons.
Eyewire gameplay is used for neuroscience research by enabling the reconstruction of morphological neuron data, which helps researchers model information-processing circuits. It is also used to generate a training dataset to further improve the artificial intelligence that assists the player through the gameplay.
A later project spawned from Eyewire is the Flywire project, which used a similar but more selective citizen science system for its tracing and annotation. Flywire builds on Eyewire and used AIs trained on the dataset produced by Eyewire players. Flywire would go on to complete and publish the first connectome of an adult fruit fly, a structure with about 140,000 neurons.
A sequel project to Eyewire, Eyewire II, was announced on March 31, 2025. It is of a similar scale to Flywire, intending to trace over 100,000 new neurons. Eyewire II is open in its alpha stages to Eyewire players ranked Scythe or higher.


== Gameplay ==
The player is given a cube with a partially reconstructed neuron branch stretching through it. The player completes the reconstruction by coloring a 2D image with a 3D image generated simultaneously. Reconstructions are compared across players as each cube is submitted, with points yielded to the players based on the agreement of their reconstruction with the developed consensus. Players are ranked on a leaderboard based on their point contributions.


== Goal ==
Eyewire is used to advance the use of artificial intelligence in neuronal reconstruction by providing a dataset from which to train and test new models. It is also hoped that the neuronal reconstruction data from Eyewire and other similar projects will result a 'virtuous cycle,' where the neuroscience discoveries achieved from analyzing real neural networks could result in improvements to artificial intelligence, and that this newer artificial intelligence could then speed up further connectomic work. 
The project is also used in research determining how mammals see directional motion.


== Methods ==
The activity of each neuron in a 350 × 300 × 60 μm3 portion of a retina was determined by two-photon microscopy. Using serial block-face scanning electron microscopy, the same volume was stained to bring out the contrast of the plasma membranes, sliced into layers by a microtome, and imaged using an electron microscope.
A number of in-progress neurons are selected by the researchers for tracing. After the player chooses which neuron to work on, the program chooses a cubic volume associated with that neuron for the player. This volume is first segmented into a number of (invisible to the player) supervoxels before an artificial intelligence performs a conservative best guess for tracing the neuron through the two-dimensional images. The artificial intelligence used is a convolutional deep learning neural network, a type of artificial intelligence often used for feature detectors. Multiple players will independently finish the reconstruction of the cube, creating a community consensus that is then submitted. These submitted consensuses are then checked by more experienced players.


== Publications ==
Kim, Jinseop S; Greene, Matthew J; Zlateski, Aleksandar; Lee, Kisuk; Richardson, Mark; Turaga, Srinivas C; Purcaro, Michael; Balkam, Matthew; Robinson, Amy; Behabadi, Bardia F; Campos, Michael; Denk, Winfried; Seung, H Sebastian (2014). "Space–time wiring specificity supports direction selectivity in the retina". Nature. 509 (7500): 331–336. Bibcode:2014Natur.509..331.. doi:10.1038/nature13240. PMC 4074887. PMID 24805243.
Greene, Matthew J; Kim, Jinseop S; Seung, H Sebastian (2016). "Analogous Convergence of Sustained and Transient Inputs in Parallel on and off Pathways for Retinal Motion Computation". Cell Reports. 14 (8): 1892–900. doi:10.1016/j.celrep.2016.02.001. PMC 6404534. PMID 26904938.
Tinati, Ramine; Luczak-Roesch, Markus; Simperl, Elena; Hall, Wendy (2017). "An investigation of player motivations in Eyewire, a gamified citizen science project". Computers in Human Behavior. 73: 527–40. doi:10.1016/j.chb.2016.12.074.


== Accomplishments ==
Eyewire neurons featured at 2014 TED Conference Virtual Reality Exhibit.
Eyewire neurons featured at US Science and Engineering Expo in Washington, DC.
Eyewire won the United States National Science Foundation's 2013 International Visualization Challenge in the Games and Apps Category.
An Eyewire image by Alex Norton won MIT's 2014 Koch Image Gallery Competition.
Eyewire named one of Discover Magazine's Top 100 Science Stories of 2013.
Eyewire named top citizen science project of 2013 by SciStarter.
Eyewire won Biovision's World Life Sciences Forum Catalyzer Prize on March 26, 2013.
Eyewire named to top 10 citizen science projects of 2013 by PLoS.
Eyewire has been featured by Wired, Nature's blog SpotOn, Forbes, Scientific American, and NPR.


== References ==


== External links ==
Official website