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flybrain

The complete central nervous system of an adult male fruit fly, Drosophila melanogaster, as a spiking network you can run on your own computer: 166,700 neurons and 25.6 million connections from the MaleCNS v1.0 connectome, wired as electron microscopy found them.

Nothing inside the brain is trained. You drive some of the fly's own neurons, step the network forward, and read out what its descending neurons (the brain's commands to the body) do.

pip install flybrain              # CPU (numba)
pip install "flybrain[gpu]"       # plus CuPy for an NVIDIA GPU (CUDA 12)
from flybrain import FlyBrain

brain = FlyBrain(device="auto")          # first run downloads the brain files (~260 MB) to ~/fly-data
left_loom = brain.cells(["LC4", "LPLC2"], side="L")   # looming detectors, left eye
giant_fiber = brain.cells(["DNp01"], side="L")        # the escape command neuron

for step in range(50):                   # one second at 20 ms per step
    fired = brain.step(inject=[(left_loom, 0.8)])
    if set(giant_fiber) & set(fired):
        print(f"left giant fiber fired at {step * brain.dt:.2f} s")

What's in it

  • FlyBrain: leaky integrate-and-fire over the whole connectome. device="cpu" | "cuda" | "auto", batch=8 runs 8 independent flies at once, plus dt, sensory_input and refractory options. brain.cells([...]) finds neurons by cell type or superclass ("descending_neuron").
  • Trace, run, Readout: reservoir computing. Collect a spike trace of any neuron population over your task, then fit a cross-validated linear or logistic PCA readout to your labels.
  • Eyes, FeatureDetectors: a visual encoder that drives the fly's visual projection neurons.

Data

The brain files live in $FLY_DATA (default ~/fly-data). The first FlyBrain() downloads them; you can also run it ahead of time:

flybrain download                 # prebuilt files, sha256-checked
flybrain build                    # or build them from the MaleCNS release (~1.1 GB; pip install "flybrain[build]")
flybrain info                     # data folder and GPU status

The first step on CPU is slow while numba compiles; later steps take about 12–15 ms on 24 threads. On an RTX 4060 a step takes 1.4 ms.

Credits and license

Code: MIT. The connectome data is MaleCNS v1.0 by FlyEM (HHMI Janelia), the University of Cambridge, the MRC Laboratory of Molecular Biology and Google Research, used under CC BY 4.0. If you use it, cite Berg, S. et al. (2026), Sexual dimorphism in the complete connectome of the Drosophila male central nervous system, Cell. The neuron model follows Fly64 by Jessica Paquette.

Source, experiments and results: github.com/alextitonis/fly.ai