Background and related work

This chapter surveys the connectome dataset, the modeling approaches borrowed, and the flight hardware.

The male CNS connectome

The Janelia FlyEM male CNS v1.0 release is a full central nervous system reconstruction of one adult male Drosophila melanogaster: brain, optic lobes, and ventral nerve cord, imaged by electron microscopy and segmented into identified neurons. It contains roughly 166,700 typed neurons and about 25.6 million significant connections, recording how many synapses link a presynaptic neuron to a postsynaptic one. Every neuron carries a predicted neurotransmitter, giving a sign to each synapse, and optic lobe neurons carry column coordinates on the eye’s hexagonal lattice, letting photoreceptors be placed in a visual field.

A separate dataset, FlyWire, reconstructs a female whole brain but lacks a ventral nerve cord, and therefore wing motor neurons: it stops at the brain’s outputs, not muscle. That completeness is why this project uses the male CNS dataset.

Whole-brain spiking models

Shiu et al. 2024 built a leaky integrate-and-fire model of the entire FlyWire brain. Every synapse’s sign came from its presynaptic neuron’s predicted transmitter, and the model had one fitted free parameter, a voltage jump of 0.275 mV per synapse. Despite that simplicity, driving sugar-sensing neurons activated the feeding motor program, and driving mechanosensory neurons activated grooming. That result is this project’s license: a signed network built directly from a connectome can reproduce real circuit behavior without tuning individual connections.

A caution carries over rather than a guarantee. Shiu et al. drove excitatory taste and touch neurons, and their success says nothing about pathways that start differently. This project’s main input is the retina, and photoreceptors are histaminergic, an inhibitory transmitter; inhibition onto a resting spiking neuron does nothing, so vision needs its own answer first.

Connectome-constrained visual models

Lappalainen et al. 2024 took a different approach: a connectome-constrained rate model of the fly visual system, trained by task optimization rather than fit synapse by synapse, that predicted neural responses with good accuracy. The result matters here because fixed connectome structure plus a small set of learned parameters can produce function that matches a real nervous system. This project differs on three axes: spiking rather than rate-based, the whole nervous system rather than one pathway, and closed loop on a physical body rather than scored against recorded activity.

The optic lobe and the eye map

Nern et al. 2025 (Connectome-driven neural inventory of a complete visual system) is the source of the column coordinates used to place photoreceptors in the visual field. Zhao et al. 2025 (Eye structure shapes neuron function in Drosophila motion vision) reports measured facet directions for the compound eye, obtained directly rather than inferred from connectivity. The current design uses a parametric eye model built from the column coordinates; the measured directions are a natural input to a future, more accurate map.

Fly flight control

A fly’s flight loop runs through the stages this design mirrors. Vision starts at the compound eyes, where each facet samples one direction; motion detector neurons T4 and T5 compute local motion, and wide-field lobula plate neurons HS and VS integrate it into estimates of the fly’s own rotation and translation. This optic flow processing is the fly’s primary source of information about drift.

Halteres, the fly’s reduced hindwings, deflect under Coriolis forces when the body rotates; sensory neurons at their base report the deflection, making halteres fast gyroscopes feeding the wing motor system. Descending neurons, about 1,300 of them, carry commands from the brain into the ventral nerve cord, reaching the wing motor neurons: 67 in this dataset, driving the power muscles and the smaller steering muscles per side that produce turns, climbs, and corrections.

The Crazyflie platform

The Crazyflie 2.1 is a 27 gram open-source quadrotor with documented firmware and a Python client library, cflib. Two expansion decks supply the sensors used here: the AI deck adds a monochrome camera with an 87 degree field of view, streaming frames over WiFi to a laptop, and the flow deck v2 adds a downward time-of-flight range sensor and an optical-flow sensor for height and horizontal velocity. The firmware’s own stabilizer stays in the loop; the brain issues setpoints for it to execute rather than driving motors directly, and cflib sends the setpoints and reads telemetry back.

Where this project sits

This project is an early attempt to put a whole-CNS connectome model on a physical body and close the loop in real flight. The wiring itself is never changed; only the boundaries, sensory encoding, motor decoding, and a few global constants, are learned, by evolution strategies rather than by hand. What that framing does and does not claim about the resulting system is the subject of the ethics chapter.

See the References chapter for full citations.