It is hungry, slightly cold, and surrounded by furniture. Its compound eyes receive the scene. Competing neural populations choose between food, warmth, light, rest, and exploration. Leg activity starts a gait. If the flight pathway wins, the wings accelerate until the body actually leaves the floor.
This is FlyLab, an interactive browser experiment built around fruit fly connectome data.
FlyLab running in the browser: the shared kitchen, neural activity, compound-eye view, needs, and movement are visible at the same time.
A connectome is a map of connections. By itself, it cannot show whether a visual signal can guide a body around a table, whether a fly can switch from walking to flight, or what happens when one neural pathway is removed.
You can observe the loop, intervene in it, and measure what changes. That makes the project useful for experimentation, education, debugging, neuro-inspired control, and visualization.
Imagine you are building a neural simulation. The fly moves, but you do not know whether the movement comes from its neural state or from a hidden steering rule.
In FlyLab, you can inspect the chain directly. Activity from descending neurons contributes to walking, steering, and flight permission. Wing power produces modeled forces. The body takes off only after those forces overcome support and gravity.
Now silence the relevant motor output. If the wings stop producing thrust, you have a causal test—not just a moving 3D model.
The same approach works for other questions: Does removing vision change the route? Does body feedback help the fly escape an obstacle? Can it walk with silent wings? Can it land when the neural goal changes to rest?
Every intervention connects a visible behavior to a measurable part of the model.
FlyLab starts with the FlyWire FAFB v783 dataset. The source connectome contains 139,255 reconstructed neurons. After model-specific nodes are added, the runtime graph contains 144,794 cells and 3,346,722 directed edges.
Those numbers are difficult to reason about as a table. FlyLab makes them observable.
One screen shows the fly’s compound-eye panorama, active neural populations, internal needs, wing output, leg contact, current goal, physical movement, and trail through the kitchen. A student can watch a stimulus change photoreceptor activity, see a goal population win, and then watch the body respond.
The interface also exposes the difference between intention and action. A fly may choose food while physically blocked by the kitchen island. The brain goal remains visible while contact feedback triggers a turn or climb response.
That distinction is one of the most useful lessons in the project: deciding what to do and successfully doing it are separate problems.
The Train button creates a second experimental fly with its own full neural graph. The main fly remains in the shared kitchen while the experimental fly enters a controlled training environment.
The user can train or test: food seeking; warmth and light seeking; obstacle escape; need-based choice; spectral cue discrimination; hypotheses about uncertain neurotransmitters.
Each browser receives a reproducible random seed. Different browsers can test different candidates and contribute results to a shared archive.