Fly Rig Anatomy

MaleCNS v1.0 · 166,700 neurons · 25.6 M edges
created 2026-09-12 · revised 2026-09-23

This project is my attempt to learn about a fruit fly's nervous system, as mapped in the MaleCNS connectome1,58 released by HHMI Janelia and Google Research in 2026, as well as an attempt to map it into a robot. The robot is a modified model Strandbeest, further rebuilt for this project.

The page explains the general system, then opens into specific elements: how the camera image is turned into nerve signals, what happens inside the connectome between eye and muscle, what a single simulated neuron does, and how neural activity becomes movement. It then discusses research used to help me understand, and a glossary of the biological terms, which are also linked in the main text where they first appear.

On the right-hand side on a wider screen, a simplified explanation of each section is shown. On smaller screens, this is a toggleable feature.

Note: This page is structured and edited by me, but for the most part is written by AI. It is intended as a set of notes, and designed to try turning AI output into a reflective learning tool, for someone like me who has typically learnt about subjects through building. I am also calling this out explicitly because many people (myself included) are very tired of AI slop, and this could comfortably be labelled as that, though that is not its intention. Also, please bear in mind that while I have some experience with neural networks, AI, and robotics (this project actually being very similar to my undergrad AI dissertation), I am ultimately just a hobbyist.

1The setup The rig in brief

The fly's brain in this project is not built or trained: it is the MaleCNS connectome1,58, the complete wiring diagram of one adult male fruit fly's brain and nerve cord, published as data files that anyone can download. Its 166,700 neurons and the connections between them are loaded into a simulation on a desktop PC, where each neuron is a simple model of a spiking cell, the kind used by the fly-brain simulation this one borrows from2.

The PC is connected to two things (Figure 1). The robot is a Strandbeest, a walking machine of linked legs, rebuilt with one motor for each side and a small controller that carries a camera and a motion sensor. The controller sends its camera image and motion readings to the PC over WiFi and turns the motors at the speeds the PC sends back. The second is a panel of 16 by 16 LEDs that lights the parts of the fly's body whose motor neurons are firing.

Between them runs a loop: the camera image becomes activity in the fly's eye neurons, the simulated brain runs, and the activity of its motor neurons is read out to turn the motors and light the panel. Input enters only where a real fly's senses would put it, and the motor neurons are only read, never set, so the robot moves only as the simulated neurons say. The next section follows one turn of that loop.

MaleCNS connectome public data release 166,700 neurons downloaded once PC runs the connectome as a simulated brain Robot Strandbeest walker camera and motion sensor two motors, left and right camera, motion motor speeds LED panel 16 by 16 cells the body map motor neuron states
Figure 1. The setup. Boxes are the parts of the rig and arrows the direction data travels. The connectome is a file, downloaded once (dashed); the PC then exchanges data with the robot over WiFi and drives the panel.

2The loop One 20 ms tick

Everything runs in one process on the PC, in a fixed rhythm: 20 ms of the fly's time per tick. Sensors are read, the brain is stepped 200 times (0.1 ms each), the outputs are sent. On the CPU one tick takes ~85 ms of wall time (0.23× real time); on the GPU it takes ~14 ms (1.46×), and the loop then sleeps so that the fly runs at exactly real time rather than ahead of its own senses. The model is the MaleCNS1 connectome, every neuron and synapse of one male fruit fly's brain and nerve cord, run as leaky integrate-and-fire units with the parameters of Shiu et al.2, the synaptic weight recalibrated on this connectome (Figure 5), in the schedule DOOMFLY59 ported them to. The one-process, fixed-tick shape is shared with DOOMFLY and Fly6460; what those systems lack is the return arrow.

Webcam / Robot cam AUKEY PC-LM1 · 640×360 M5 AtomS3R-M12 · 640×480 PC · Windows (numba kernel) or WSL2 (GeNN kernel on the RTX 3070 Ti) Medulla encoder optic flow → T4/T5 expansion → LC4/LPLC2 Proprio encoder tilt → JO-CE · gyro logged motor speed → chordotonal Brain 166,700 LIF neurons 25.6 M synapses step(drive, 20 ms) → spike counts[n] numba on CPU · 0.23× GeNN on GPU · 1.46× LED decoder 815 motor neurons → 256 cells Motor decoder leg pools L / R → speed −100…100 USB · 30 fps drive (mV) drive (mV) counts 16×16 body map Sparkle Motion · WLED · 256 WS2812B DDP · UDP 4048 ≤ 50 fps · 768 B Strandbeest M12 S2 S4 IMU · camera 120° BST1 · UDP 5006 50 Hz · L, R + state TEL1 · UDP 5007 · 100 Hz · gyro, accel, actual motor speeds FRM1 · UDP 5010 · the beest's own camera · 3 to 5 fps on the floor · rotated and mirrored one tick = encoders write drive → brain.step(20 ms) → decoders read counts
Figure 2. The whole loop. Boxes are the parts of the rig, each labelled with its hardware and rates. Solid arrows are the forward path, run once per 20 ms tick; the one dashed arrow is the only feedback, from the beest back into the fly's senses.

The frames in Figure 2 come from a webcam on the desk or, on the floor, from the beest's own controller, an AtomS3R-M12 whose 120 degree camera sends JPEG frames over WiFi: 13 to 15 per second on the desk, 3 to 5 per second on the floor, where the radio link sets the rate. The panel gets a frame per tick over DDP61; the beest gets a command per tick whether or not it changed, and 500 ms of silence stops its motors. The table lists each link.

The dashed arrow carries the beest's IMU readings and actual servo speeds back at 100 Hz, into the senses a walking fly uses for the same job. A walking fly reads its own turning from three places: the whole visual field sweeping past, the strain and movement sensors in its legs, and its antennae3,4. The rig feeds the first from the camera and the second from how fast the wheels are actually turning, as the leg-position sense of the chordotonal organs; the antennae get the tilt, as drive on the gravity-sensing cells of Johnston's organ (section 6 has the details). The gyro is read and logged while nothing enters the haltere cells. A fly's halteres are its flight gyroscopes5; they only work while they are beating, and a walking fruit fly holds them still, does not move them on a shaking floor, and climbs and falls the same with them removed6.

Table 1. The links. What travels between the PC, the panel and the beest, by protocol, rate and payload.
LinkProtocolRatePayload
Webcam → PCUSB / OpenCV30 fps640×360 BGR; optic flow computed at 192×108
beest camera → PCFRM1, UDP 501013–15 fps desk · 3–5 fps floor640×480 JPEG in 1,400-byte datagrams; rotated 270° and mirrored left to right on the PC
PC → panelDDP, UDP 4048≤ 50 fps10-byte header + 256 × RGB; WLED62 maps to the serpentine strip
PC → beestBST1 / BST2, UDP 500650 Hzleft, right (−100…100), a state byte (BST2), 25-byte body summary, sequence, xor checksum
beest → PCTEL1, UDP 5007100 Hzgyro xyz, accel xyz, left/right actual, sequence, xor checksum

3Breakaway: what the fly sees Medulla encoder

The camera frame is split down the middle into a left and a right eye. On the floor the frame is the robot's own: the M12's camera sits sideways in the base and its sensor reads out mirrored, so each frame is rotated by 270 degrees and flipped left to right on the PC before the split, which puts what lies on the beest's left in the left eye and what lies on its right in the right eye. The encoder does the job the fly's first three visual stages would do, because in the spiking model those stages don't transmit (Figure 4), and injects current where the pathway becomes spiking: the motion detectors (T4/T5)7, the looming detectors LC4 and LPLC2, which carry two different quantities, the approaching object's angular velocity and its angular size8-10, and, since 14/09, two further lobula outputs that the connectome says carry the signals the model was missing: the lobula-plate projection cells LLPC1 and LPC1, which take almost all of their input from the front-to-back and back-to-front motion layers and are a direct lobula-plate input to the steering neuron DNa02 (72 LLPC1 cells here; in the fly its dominant inputs are the central-complex PFL3 outputs and other descending neurons)1,11,63, get the same per-eye flow; and the small-object cells LC10c/LC10d12,13, a family whose biological role is courtship tracking through LC10a12,63 and which in this graph reach the pursuit neuron DNp09 on the right side only (its documented input is LC914,63), get a detector for small dark things moving against the background15, and since 15/09 the same detector drives LC9 on its own smaller size band12. Where each signal goes was read off the wiring, not chosen; Figure 3 shows each channel and the cells it drives.

Motion is measured as optical flow with the Farnebäck16,64 algorithm, and each eye's flow drives the matching direction-selective T4/T5 subtype: front-to-back motion is leftward image motion in the left eye and rightward in the right eye. Looming is the radial expansion of that flow about each eye's centre, taken as a median rather than a mean, required to be coherent across most of the eye and to persist for 120 ms, with a slow per-eye bias removed; an early version that used the mean fired on a third of all frames from ordinary movement, so the fly jumped at everything. From that one expansion the encoder reads two numbers. The angular size of the expanding patch (twice the 90th-percentile radius of the region whose outward rate clears the threshold, so a few stray pixels do not read as a full-field loom) goes to LPLC2 at 0.2 mV per degree, and its growth rate, size times the expansion rate, goes to LC4 at 0.35 mV per degree per second, each capped at 30 mV, because the Giant Fiber's looming response is a linear function of angular velocity supplied by LC4 plus a function of angular size supplied entirely by LPLC2, the cell whose dendrites read outward motion in all four directions and nothing else9,10. One deviation is recorded: the size term is linear up to its ceiling, where Ache et al. fit a Gaussian, because the Gaussian's falling limb is a property of the Giant Fiber's summation, not of LPLC2. Under the live model the calibrated synthetic zoom (LC4 30 mV, LPLC2 26 mV) fires the Giant Fiber within the first 20 ms of the approach and holds it at 300 Hz for as long as the drive lasts, with the escape descending neurons DNp02, DNp04 and DNp11 at 239, 310 and 215 Hz; a desk lean-in seen by one eye gives 142 Hz. Angular velocity alone recruits the whole escape set, angular size alone the Giant Fiber and DNp04 only. The small-object channel reaches the pursuit neuron DNp09 as it does in the fly, as an onset burst rather than sustained walking: two spikes in the first 20 ms and nothing more (finding 7, and question 3 in section 10).

left eye · 150° right eye · 150° 15° binocular overlap at the midline translation fx expansion div Optic flow Farnebäck · 192×108 per eye: fx, fy, divergence and patch size 60 ms low-pass Small moving target motion contrast, size-gated blobs 8–100° across, moving ≥ 12 °/s 1 mV per °/s same flow 0.35 mV per °/s 0.2 mV per ° 15 mV full strength T4 / T5 motion detectors a · front-to-backb · back-to-front c · upwardd · downward ~1,700 cells per subtype per eye LLPC1 + LPC1 lobula-plate cells 254 per eye · steering (DNa02) LC4 + LPLC2 looming detectors LC4 · angular velocity of the object LPLC2 · angular size of the object 311 cells · fire the Giant Fiber LC9, LC10c + LC10d small-object cells 688 cells · tracking (LC9 → DNp09) thresholds: motion > 5 °/s · expansion > 0.15 /s (camera noise floor 0.07) · loom drives capped at 30 mV target contrast ≥ 15 levels · LC10 band 8–100° · LC9 band 8–60° R1–R6 photoreceptors · 3,335 cells luminance path · off by default brightness per eye column (1,780 columns) → Brain as tonic drive (mV)
Figure 3. A medulla stand-in. Left to right: the camera frame split into two eyes, the optic flow and the small-target detector computed from each, and the cell populations they drive, each arrow labelled with its gain. The grey dotted path along the bottom is the photoreceptor route, drawn but switched off.

The grey dotted path in Figure 3 is the literal retina model, brightness into photoreceptors, which works as far as the lamina and then stops, so it is off. This is the point where this project departs from every other MaleCNS game demo: DOOMFLY, Fly64 and flybrain.online65 all inject at the photoreceptors or lamina and then need extra help (DOOMFLY a 12 mV tonic current into the lamina, Fly64 a background drive and noise on every neuron) and still report silent or weak downstream responses. We measured why (Figure 4) and moved the injection point past the dead stages.

4Breakaway: inside the connectome Signal path, sense to muscle

The brain is not a black box; it is 166,700 named cells in known groups. Figure 4 follows the visual signal from the eye to the legs and marks where the spiking model breaks down (grey, dotted), where we inject instead (bold arrows), and the loop that latches once lit. Commands leave the brain through descending neurons; the Giant Fiber is the escape command; the ventral nerve cord is the insect equivalent of a spinal cord.

OPTIC LOBE · 89,403 + 6,098 cells CENTRAL BRAIN · 32,164 cells VENTRAL NERVE CORD · 13,161 cells R1–R6 photoreceptors graded · histaminergic Lamina L1 L2 L3 responds · correct sign Medulla Mi1 Tm1 Tm2 Dm Dm amacrines clamp it ✕ nothing gets past here T4 / T5 13,580 cells · 4 directions LC4 · LPLC2 311 cells · looming inject inject Lobula plate tangentials HS · VS · wide-field flow direct to the Giant Fiber Premotor · LAL, PS, GNG turn and walk commands LAL046/083 inhibit DNa02 R Antennal lobe · Mushroom body Kenyon cells · APL · DPM · MBONs Kenyon cells 0 Hz · APL ~30 at rest under every pulse Ring loop · 4 modules DNg33 · FR1 · GNG016 · VNC 270–390 cells ≥ 100 Hz after every pulse compass ring sparse, ~6 Hz every pulse into silence ends in the ring loop its reciprocal pairs re-fire each other at the ceiling 1,314 descending neurons GF · DNp01 DNa02 L / R DNp09 MDN escape · steer · forward · backward VNC circuits T1 T2 T3 leg premotor wing · neck · abdominal GF → TTM jump muscle 815 motor neurons proboscis · neck 107 wing · haltere 88 legs ×6 384 abdomen 236 Proprioceptors halteres 205 · not driven chordotonal 425 · JO-CE 475 ← beest IMU + speeds Labellar taste neurons, typed by modality sugar 34 · water 17 · high salt 26 · bitter 38 test stimulus → proboscis MNs · MN9
head, neck · proboscis row
wings, halteres
legs, front → hind
abdomen A1 → A10
Figure 4. Where the visual signal goes. Boxes are cell populations, laid out as optic lobe, central brain and nerve cord. Grey dotted: the stages that go silent in the model; bold arrows marked "inject": where the encoder puts current instead; ochre: the ring loop; dashed: feedback from the beest. The swatches colour the motor neurons by body part.

The grey chain at the top left of Figure 4 is the real retina, lamina and medulla. Their cells are graded (non-spiking) in the fly17 and go silent in a spiking model: visual projection neurons stayed at 0.9 Hz whatever the image. It is the same wall that DOOMFLY's notes record when its biological readouts stayed silent under visual input. From the injection points on, the wiring works: T4/T5 subtype a in both eyes gives DNa02 left 26 Hz against right 2 Hz, and the calibrated loom gives the Giant Fiber 300 Hz.

Ochre marks the one state a pulse into silence ends in, the ring loop. It is a set of small modules rather than one loop, each a group of cells whose connections to each other are strong enough that one or two spikes of a partner carry a cell back over threshold, so that each holds itself at the refractory ceiling (finding 13). Under the live model four remain: the two DNg33 descending neurons, which share more than seven hundred synapses each way; FR1, a column type of the fan-shaped body wired all-to-all within each side; a taste cluster in the gnathal ganglion around GNG016; and, after a sugar pulse, an abdominal pair of the nerve cord, INXXX137 and INXXX181, with its motor neurons. In the last second of a run after a T4/T5 pulse DNg33 fires 212 Hz, GNG016 221 and FR1 163, with 304 cells at or above 100 Hz and the whole network at 1.3 Hz; LC9, loom and sugar pulses end in the same modules (268 to 391 cells at or above 100 Hz, the network at 1.2 to 1.3 Hz), and none releases within a 4 s run. The compass ring itself holds only a sparse pattern after the pulse: the EPG compass neurons at 6 Hz on average, their peak columns at 16 to 29 Hz, and the PEN_a shifting neurons silent. That is because the live model signs GLNO, the compass's noduli input, inhibitory as measured (Figure 5); signed excitatory by the transmitter policy, GLNO and PEN_a excite each other into a fifth module that carries the whole ring to the ceiling (PEN_a 198 Hz, ExR6 261, 440 cells at or above 100 Hz).

The antennal lobe and mushroom body, drawn without ochre, stay at rest under every one of those pulses: the Kenyon cells fire no spike in any 20 ms tick, the projection neurons about 0.5 Hz, and the mushroom body's two giant cells, APL and DPM, tick over at 31 to 43 Hz after a visual pulse and 11 to 40 Hz after sugar, driven from outside the Kenyon cells (finding 11). Two settings of the live model keep them there, and the lobe and the mushroom body are separate questions (Figure 5). Signing the 67 antennal-lobe local neurons that have no transmitter prediction by their class's majority, which is inhibitory, keeps the lobe dark under a sugar pulse (projection neurons 0.4 Hz where the build's excitatory sign gives 49); the Kenyon cells' measured gap to threshold keeps them silent against whatever does reach them. Neither setting does both jobs alone (finding 3). Under the build's excitatory sign, taste and most lobula pulses ignite a joint antennal-lobe / mushroom-body loop (APL 385 Hz, DPM 354, Kenyon cells 47 Hz), and with the connectome's predicted transmitter for lLN1_bc that loop is thirty antennal-lobe local neurons exciting themselves, which at the calibrated weight a visual pulse ignites too; the lineage table gives the size of both. The live model signs those thirty cells GABAergic, as curated63, a call the classifier's own paper leaves open (finding 5); the sign of the DPM cell, curated GABA and serotonin where the connectome predicts dopamine18,63, changes nothing that matters.

The sugar path at the bottom of Figure 4 is the validation stimulus from Shiu et al.2,19, the same experiment eonsystems/fly-brain66 uses as its benchmark case: the labellar taste neurons of the male connectome, typed by modality20 as 34 sugar, 17 water, 26 high-salt and 38 bitter cells, onto the proboscis motor neurons and MN9, the rostrum-protraction motor neuron the Shiu experiments read. One second of sugar drive takes the proboscis pool from silence to 40 Hz and MN9 to 89.5 Hz, its left cell well ahead of the right (finding 10); after the drive MN9 falls to 23 Hz within 0.3 s and is silent by the end of a 4 s run. Findings 6 and 12 give the other modalities and bitter's suppression of the sugar response. High salt drives MN9 harder than sugar, a connectome-level result rather than a fault to tune away: the graph encodes connectivity, not valence.

5Breakaway: one neuron, one synapse Leaky integrate-and-fire

Every one of the 166,700 cells is the same simple unit, drawn in Figure 5. Its voltage drifts toward rest and is pushed by arriving input; at threshold it fires, resets, and ignores everything for 2.2 ms (the refractory period). Each spike reaches its targets 1.8 ms later with a strength set by the synapse count in the electron-microscopy data times 0.31 mV per contact. What differs between cells is who they connect to and how strongly (the connectome) and the sign, guessed from each cell's neurotransmitter (acetylcholine excites; GABA, glutamate and histamine inhibit in the fly)2,18. The constants are from Shiu et al.2, with five recorded deviations, each set from Shiu's own criterion, a curated transmitter or a measurement and none tuned for behaviour: the weight, three signs and one threshold, taken in turn below.

−45 mV threshold −52 mV rest Kenyon cells −30.5 mV v time → refractory 2.2 ms excitatory arrivals push v up (+g) inhibitory arrival (−g) spike leak: v decays to −52 mV with τ = 20 ms · synaptic g decays with τ = 5 ms · simulation step 0.1 ms A B axonal delay 1.8 ms Δg = sign(A) × contacts × 0.31 mV dropped if B is refractory sign of A, from its predicted transmitter + acetylcholine · 103,720 cells − glutamate 29,302 · GABA 22,069 · histamine 7,891 + assumed: dopamine, serotonin, octopamine, unclear · 3,647 − unclear, class majority inhibitory: 67 antennal-lobe local neurons − lLN1_bc local neurons, 30 cells: GABA as curated, whatever the prediction − GLNO noduli inputs, 4 cells: inhibitory, as measured onto P-EN1 weights stored once as CSR: ptr[n+1] · post[E] · weight[E] · 205 MB
Figure 5. The unit. Left, one cell's voltage over time against its rest and threshold lines: excitatory arrivals push it up, an inhibitory one pulls it down, and after a spike it is held through the refractory period. Right, one synapse from cell A to cell B, with its delay, its weight rule and the signs by transmitter.

The weight is the model's one free parameter. Shiu et al. fitted it at 0.275 mV on the FlyWire female brain, choosing it so that sugar receptor neurons firing at 100 Hz drive the rostrum motor neuron MN9 to roughly 80 % of its maximal rate2. Repeated on this male connectome, with the receptor neurons held at 100 Hz and MN9 read against its plateau at 150 to 300 Hz, 0.275 mV gives 68 % and the criterion is met between 0.30 and 0.32 mV, a band rather than a constant because a two-cell readout in a deterministic model scatters by 10 to 15 points between neighbouring values (finding 4). The model runs at 0.31 mV, about 13 % above the borrowed value; within 30 % of it the pathway keeps working in the same sense, and below about 0.25 mV the model changes regime rather than weakening. Under the live model, with the signs and the threshold below, the criterion holds at 0.31: sugar receptor neurons at 100 Hz give MN9 95 Hz against a plateau of 99, 96 % of its maximum and 97 % of its mean.

The sign is the predicted transmitter's, with two named cell types set from the literature and one rule for cells the classifier leaves undecided. The thirty lLN1_bc local neurons of the antennal lobe, predicted cholinergic at a confidence of 0.75 and curated GABAergic63, are signed GABAergic, because with the predicted sign their self-excitation is a loop the whole model falls into (Figure 4). The four GLNO cells, which bring input from the lateral accessory lobe into the noduli, onto the compass's shifting neurons, have no confident prediction in this connectome and are signed inhibitory: Franconville, Beron and Jayaraman activated GL-N1, the same cell type under the name Virtual Fly Brain lists as its synonym, and saw the compass's P-EN1 cells inhibited21,63, the central-complex connectome describes the noduli inputs as likely inhibitory22, and two other connectomes predict glutamate or GABA for it at low confidence63. Signed excitatory, GLNO and the shifting neurons excite each other into a module that holds the whole compass ring at the ceiling (Figure 4). And a cell with no transmitter prediction at all, which Shiu's policy signs excitatory as it does the dopaminergic, serotonergic and octopaminergic cells2, takes the predicted majority sign of its annotation class. In this connectome that re-signs exactly one group: the 67 antennal-lobe local neurons without a prediction, whose class is predicted 214 inhibitory to 137 excitatory, become inhibitory, which is how the literature describes the class, mostly GABAergic with a large glutamatergic minority that also inhibits and a few cholinergic cells23-25. Every other class with unpredicted cells, the central complex's included, has an excitatory or tied majority, and no weight changes in size. Signed excitatory, those 67 cells alone hold the projection neurons at ten times their resting rate (finding 1).

One intrinsic constant differs by cell type. Every cell fires 7 mV above rest, Shiu's value, except the 4,064 Kenyon cells of the mushroom body. Turner, Bazhenov and Laurent patched Kenyon cells in the fly and measured 21.5 ± 5.6 mV from rest to spike threshold (n = 17), with 1.4 mV per projection-neuron input, so about 15 inputs must coincide26. Given that gap, a Kenyon cell here needs 26 synchronous projection-neuron spikes at the graph's mean connection of 5.35 mV instead of 9, and the mushroom body rests under every input the antennal lobe produces while the feeding calibration and the reference responses stay as they were. Per-type intrinsic parameters are ordinary in cell-type models: time constants shared within a type in a connectome-constrained model of the fly's visual system17, generalized integrate-and-fire fits per type in mouse cortex27, electrical types in a reconstructed cortical column28. Here the one class with a measured gap gets it, applied to all of its cells. The gap is one of three Kenyon-cell properties in that measurement and the only one taken: the input resistance above 10 GΩ and the membrane time constant above 200 ms have no counterpart in this unit, and under background input the model's Kenyon cells fire nothing where the fly's fire 0.1 Hz with a fifth of them silent.

Three further terms exist in both kernels and are switched off in the live model. Since 14/09 each cell can carry adaptation29: every spike adds a small amount to a per-cell variable that is subtracted from its drive and decays away, so a cell tires under sustained input. How much and how fast is set per cell class from the physiology where it exists and by inference from the class elsewhere (27 rules over the annotation table: photoreceptors and Kenyon cells none17,30, olfactory neurons strong and fast31, bristle neurons likewise by inference from campaniform recordings in other insects, central neurons moderate27-29,32,33, motor neurons weak and slow34, descending neurons inferred from the behavioural persistence of their activation14), not tuned to the robot. The live runs have had it off since 15/09: on the benchmark it releases the ring loop of Figure 4 after both test pulses, and it takes the feeding readout to a third of its size (finding 14, question 5). Since 18/09 a cell can also receive a background of spontaneous synaptic events at a rate set per cell class from the physiology; that term is off for the reason given in question 1 of section 10. Since 19/09 the olfactory receptor neurons' output synapses can carry the short-term depression Kazama and Wilson measured35; it is off as well (finding 1).

6Breakaway: from motor neurons to light and legs Decoders

Both outputs read the same 815 motor neurons. The panel shows all of them, arranged as the body; the beest's throttle reads the two leg pools, and its steering, since 13/09, reads the descending neuron DNa02 (left minus right) one synapse upstream, because that is where the model carries the turn cleanly while the leg pools' left-right difference is washed out. Neither shows raw rate: a slow baseline is subtracted so a change in what the fly is doing is what you see, not its resting hum. Reading motor neurons is unusual: DOOMFLY, Fly64 and flybrain.online all decode a handful of descending neurons (DNa02 for turning, DNp09 or DNg100 for forward, MDN for reverse; flybrain.online also reads DNa01, which in the fly is a second turning neuron63) with hand-set gains; only desktop-fly67 reads MaleCNS motor neurons, for its leg animation. We keep the descending-neuron decoder as an alternative mode; its forward pool is DNp09 with DNa01, the list flybrain.online uses. Two readout rules were added after floor tests: any non-zero command is dead-banded before it is raised to the servos' minimum speed, and a reverse command requires the backward-walking neuron MDN36 to out-spike the forward one for a whole window, because a two-tick MDN burst at a loom had driven a third of a second of full reverse.

815 motor neurons spike counts per 20 ms 409 left · 406 right Body map · rows are anatomy, columns are left | right rows 0–1 · proboscis, neck rows 2–3 · wings, halteres rows 4–9 · front, middle, hind legs16 muscle groups per leg, coxa → tarsus rows 10–15 · abdomen A1 → A10 fly's leftfly's right value = 1 − exp(−(rate − baseline) / 40 Hz) · gamma 2.2 · master 0.25 baseline τ 5 s, 15 % of it still shown · → DDP at ≤ 50 fps all 815 → 256 cells Motor decoder · leg pools left = mean rate of 192 left-leg MNs right = mean rate of 192 right-leg MNs − baseline (τ 4 s) · dead band 0.05 reverse if MDN out-rates DNp09 any non-zero output → 65…100 (the servos ignore less) leg pools only S2 · left servo1500 − v·10 µs S4 · right servo1500 + v·10 µs BST1 AtomS3R-M12 in Atom Motion ESP32-S3 · BMI270 IMU · camera 120° slew 20 / 20 ms · watchdog 500 ms button = e-stop camera frames to the PC, UDP 5010 3 to 5 fps on the floor over WiFi Proprio encoder → Brain tilt → JO-CE · gyro logged actual speed → chordotonal TEL1 · 100 Hz mount rotation [-90, 0, 0] from the tilt and yaw tests
Figure 6. Two readouts of one output. From the 815 motor neurons on the left, the upper path pools all of them into the body map's cells and the lower path only the leg motor neurons into two servo speeds for the beest. The dashed arrow on the right is the return path into the fly.

The beest's two speeds drive continuous-rotation servos, one per side. The dashed return path in Figure 6 is what closes the loop. Tilt becomes drive on the gravity-sensing cells of Johnston's organ (JO-CE), roll split between the two antennae and pitch to both, the cells that answer a steady deflection as gravity and wind do3; each side's actual speed becomes that side's leg-position sense, the femoral chordotonal organ, whose cells encode joint position, movement direction and speed4. The gyro is logged and drives nothing, for the reason given in section 2; in this graph the haltere cells are a wing pathway, with no direct connection to the steering neuron DNa02 and a net inhibitory two-step route to the DNa02 on their own side (finding 8).

Measured, not assumed: driven through T4/T5, the model turns left more readily than right (DNa02 left 35 Hz vs right 4 Hz), with extra inhibition reaching the right cell from four named premotor cells. Driven through LLPC1, a lobula-plate cell with a direct synapse onto DNa0263, both sides fire alike (122 Hz on the right for rightward motion), so the asymmetry belonged to the T4/T5 route, not to DNa02.

7The body map, cell by cell LED map

Section 6 shows the panel as sixteen rows; Figure 7 is the same map at the resolution of single LEDs, so that any cell on the physical panel can be traced to the motor neurons that light it. It is generated from the map the LED decoder loads at runtime and the MaleCNS annotation table, and checked against the tool that builds the map.

Rows run from the head down: row 0 proboscis and pharynx, 1 neck, 2 wing, 3 haltere, 4 and 5 front legs, 6 and 7 middle legs, 8 and 9 hind legs, 10 to 15 the abdomen by segment. Each row is mirrored about the midline: the first muscle-group bin sits at c7 on the left and c8 on the right, and later bins step outward, so left bin k is at column 7-k and its right twin at 8+k. Each leg pair spans two rows of 16 bins, from proximal (the coxa promotor, at the midline of the first row) to distal (the tarsus, then a catch-all at the edge of the second row); the four grey cells in each of the middle and hind leg pairs are bins whose muscle group has no annotated motor neuron in that segment. Neck and abdominal neurons carry no muscle-group annotation and are dealt round-robin into their eight cells, so those cells are stable but arbitrary. On the panel the two cells of the jump muscle's motor neurons (TTM, r2 c5 and c10), which carry the loom response, render white at their own ceiling instead of their row's hue.

The map holds 815 motor neurons on 248 of its 256 cells; 8 cells are empty. Viewed from the front, with the LED strip starting at the panel's top-left corner, the table is the panel: row 0 at the top, column 0 at the left edge.

c0c1c2c3c4c5c6c7c8c9c10c11c12c13c14c15
r0184852412214365220proboscis / pharynx (pm, am, rm)
r12323333333333232neck (nm)
r24544317557134455wing (wm)
r33111112112111113haltere (hm, xm)
r48241422442221427front leg T1, proximal bins
r587210542332659279front leg T1, distal bins
r67211423--3241127middle leg T2, proximal bins
r718-265322223562-18middle leg T2, distal bins
r81311422--2241135hind leg T3, proximal bins
r922-289218422882-20hind leg T3, distal bins
r103344454444544433abdomen T3+A1 (ad)
r111112223223222111abdomen A2
r121111122222211111abdomen A3
r131111122222211111abdomen A4
r141113123333213111abdomen A5-A7
r152323623454243223abdomen A8-A10 / terminalia
Hover a cell
Each cell sums the spikes of the motor neurons behind it, per neuron per second; brightness follows that rate minus a slow baseline, the hue is fixed per row. Number = motor neurons in the cell; a dash is a cell with none. Click a cell to highlight its muscle group on both sides.
Figure 7. The map, cell by cell. Rows run from head (top) to abdomen (bottom); columns c0 to c7 are the fly's left and c8 to c15 its right. A cell's number counts its motor neurons; grey cells with a dash have none. Hover a cell for its neurons; click it or pick a muscle group to highlight both sides.

8Lineage: what was borrowed, what was measured, what is new

The design was built on a reading of the other simulators that appeared around the MaleCNS release1 (September 2026) and the earlier FlyWire work30,37 they descend from. The table is the audit trail: for each element, where it came from and whether this project aligns with or departs from the source, and why.

Table 2. Lineage. Each part of the rig, the work it came from, and how this project stands to it.
ElementSourceRelationship
Neuron model and constantsShiu et al.2 (FlyWire37; Brian2; code68)Aligned, with five recorded deviations. τm 20 ms, τsyn 5 ms, −52/−45 mV, 2.2 ms refractory, 1.8 ms delay, 0.1 ms step2, the only parameter set with a peer-reviewed validation behind it (feeding, grooming and halting circuits19,38,39). The weight per contact is 0.31 mV, recalibrated on this connectome by Shiu's own criterion (0.275 gives 68 % of maximal MN9 firing at 100 Hz sugar input here; 0.30 to 0.32 meets the 80 %), the lLN1_bc antennal-lobe local neurons are signed GABAergic as curated63 where the connectome predicts acetylcholine, the four GLNO noduli inputs are signed inhibitory as measured onto the compass's P-EN1 cells21,22 where the connectome leaves their transmitter undecided, the 67 antennal-lobe local neurons with no transmitter prediction take their class's predicted majority sign, inhibitory, where Shiu's policy signs every unpredicted cell excitatory23,25, and the Kenyon cells fire 21.5 mV above rest, Turner et al.'s measured gap, where Shiu gives every cell 7 mV26 (Figure 5). Recorded and not adopted: Virtual Fly Brain's cell-type classes give GNG016 GABA and AN27X013 glutamate where the model signs both excitatory by policy; both class entries are other connectomes' predictions written into the ontology rather than measurements18,63, so each was measured and left as it is (GNG016 at GABA removes one module of the ring loop, AN27X013 at glutamate changes nothing; question 1). The kernel reproduces the sugar → proboscis experiment (from silence to 40 Hz on the proboscis motor neurons and 89.5 Hz on MN9), and Shiu's validation, carried to this connectome by cell type, reproduces 120 of his 164 predictions (finding 12).
Kernel scheduleDOOMFLY59 (MIT), doom/engine.pyAligned, then extended. Same event-driven CSR kernel, same reset-at-delivery schedule and “unless refractory” semantics, adapted with attribution. Ours adds an active set with parking, checkpointing, and a GPU twin with cross-kernel state transfer.
Graph constructionDOOMFLY datasets.json node/edge policyAligned, verified. Same node policy (assigned superclass, glia excluded), same edge policy (all edges, autapses kept). Our build reproduces DOOMFLY’s counts exactly: 166,700 neurons, 25,582,938 edges, 124,177,617 contacts, 3,718 uncertain-sign cells treated as excitatory. The live model re-signs 71 of them at load, the 67 antennal-lobe local neurons by their class's majority and the four GLNO cells by measurement (Figure 5).
Photoreceptor eye columnsDOOMFLY (receptor positions inferred from hex-annotated L1/L2/L3 targets)Aligned. Same inference; 3,335 receptors mapped and 42 not, DOOMFLY’s numbers. Then found not to matter, because the path is dead in LIF.
Where vision is injectedDOOMFLY59, Fly6460, flybrain.online65, mrfly69: photoreceptors or L1/L2Departs. Measured that visual projection neurons stay at 0.9 Hz whatever the image under photoreceptor drive, and that T4/T5 never fire from any Mi1/Tm drive. Inject at T4/T5 (4 directions per eye) and LC4/LPLC2 instead; both reach descending neurons (DNa02 26 vs 2 Hz; Giant Fiber 302 Hz on the calibrated loom). Extended 14/09 with LLPC1/LPC1 (flow) and LC10c/d (small moving object), each chosen because the graph shows it reaching a command neuron (LLPC1 onto DNa02 on both sides, LC10c/d onto the right DNp09 only), and 15/09 with LC9, the documented DNp09 input14,63. No other MaleCNS simulator injects at lobula outputs.
Background drive and noiseFly64 (tonic drive + Bernoulli noise on all neurons; dt 20 ms)Tested twice and rejected. 3–6 mV tonic + 1 mV noise lights the whole network but adds no image dependence (visual projection neurons flat across dark / left / right stimuli). Fly64’s 20 ms step also makes the network a rate model; we keep 0.1 ms. The second test, random synaptic events per cell class at the spontaneous rates the literature reports (section 10, question 1), gives either silence or the full attractor and loses the feeding and small-object responses; under the live model the background alone holds the ring loop of Figure 4 before any stimulus (network 2.8 Hz) with the antennal lobe and the mushroom body at rest. The term exists in both kernels and is switched off.
ReadoutDOOMFLY (DNp20, DNpe017; alt. DNa02/DNp09/MDN/MN9), Fly64 (DNg100, DNa02/DNg13, DNp01/DNp10), flybrain.online (DNa02/DNa01/MDN/DNp09; DNa01 is a turning neuron63)Departs. Primary readout is the 815 motor neurons pooled by muscle group: the VNC’s result rather than the brain’s command. Since 13/09 a hybrid: throttle from the leg pools, steering from DNa02 left minus right, so the turn is read where the model carries it; that part is aligned with the DN decoders of the other demos. The DNa02 right-side suppression seen under T4/T5 drive is absent under LLPC1 drive.
Closed loopdesktop-fly67 (looming from cursor, motor rates → leg joints); all game demos open-loopNew. Real IMU and servo speeds return into the fly: tilt into the gravity-sensing JO-CE cells, each side's speed into its leg chordotonal cells3,4; the gyro is logged and drives nothing, because a fruit fly's halteres are flight sensors held still while walking5,6. In the graph the haltere afferents are a wing pathway (+5.2 mV per cell onto the wing motor pool at one hop against +0.3 onto the leg pool) with no direct connection to DNa02. Harness result: the closed loop raises the leg motor neurons by about 1.5 Hz in the second after a stimulus (6.2 and 5.9 Hz against 4.8 and 4.1 open) and settles within 6 s, the chordotonal cells silent and the command off in the last second; the engineered gyro-to-haltere path it replaced never settled (chordotonal cells at 78 to 89 Hz, the command still on), a positive feedback loop. No gait rhythm either way.
Network latchDOOMFLY logs (“network stays busy”), kernel agent reportCharacterised. Under the live model one state, and it does not release: every pulse tried, visual, lobula, loom or taste, ends in the ring loop, four small self-sustaining modules (the DNg33 pair at 212 Hz, FR1 at 163, a gnathal taste cluster, and after sugar an abdominal pair; 268 to 391 cells at or above 100 Hz, the network at 1.2 to 1.3 Hz) with the compass ring sparse and the antennal lobe and the mushroom body at rest (finding 13). Signed excitatory, GLNO adds a fifth module that holds the whole compass ring at the ceiling (440 cells, 1.5 Hz). Taste and most lobula pulses latch an antennal-lobe / mushroom-body loop only under the build's excitatory sign for unpredicted cells (half strength with lLN1_bc curated: Kenyon cells 47 Hz, 2,800 cells above 100 Hz, the network at 4.2 Hz; full strength with lLN1_bc as predicted: 136 Hz, 6,350 cells, 8.2 Hz); the class-majority sign removes it and the Kenyon cells' measured gap keeps them out of any antennal-lobe loop that lights (Figure 4). Panel and motor decoders subtract a slow baseline. Under per-class adaptation (below) the ring loop releases 1.5 s after a sugar pulse; after a visual pulse the DNg33 pair holds at 120 Hz (question 5).
Loom detectorDOOMFLY and desktop-fly: looming from an object's size on screen or the cursor; no camera-based loom in any demoEngineered. Robust radial expansion per eye (median, coherence gate, 120 ms integration, slow bias removal), thresholds set from desk and walking recordings so that ordinary motion gives zero false looms; drives LC4 with the expanding patch's angular velocity and LPLC2 with its angular size, the two quantities Ache et al.9 found the Giant Fiber summing, each with its own gain. The size term is linear up to its ceiling where their fit is Gaussian, because the falling limb belongs to the Giant Fiber's summation, not to LPLC2.
Spike-frequency adaptation, per cell classMechanism: AdEx (Brette and Gerstner29). Per-type parameters: Allen GLIF fits (Teeter et al.27), Blue Brain e-types28, and for the fly Lappalainen et al.17 (time constants shared within type). Values: fly physiology per class where it exists (ORNs after Nagel and Wilson31; motor neurons after Azevedo et al.34; visual projection neurons after Wu et al.8; the central complex after Turner-Evans et al.40), inference from the class elsewhere (descending neurons from the behavioural persistence in Bidaye et al.14; bristle neurons from campaniform recordings in other insects; central and premotor interneurons after the regular-spiking classes of AdEx, Izhikevich and Potjans and Diesmann29,32,33).New for a whole-CNS simulation. None of the MaleCNS or FlyWire demos (DOOMFLY, Fly64, flybrain.online, desktop-fly, Shiu et al.'s own model) has adaptation; Shiu's LIF is memoryless. Ours: 27 rules over the annotation table, strengths from the physiology where it exists and never tuned to the robot, switchable (off / uniform / per class). Effect measured in the harness under the live model: the ring loop releases 1.5 s after a sugar pulse and holds on the DNg33 pair after a visual one (with GLNO excitatory it released every pulse within 0.5 to 1.2 s), the escape response is kept (unlike uniform adaptation, which flattened the sensory responses), and the feeding readout falls to a third of its size, below Shiu's criterion (question 5).
GPU kerneleonsystems/fly-brain66 benchmarks (GPL-2; reference only): GeNN 2.0× on an RTX 4070, Brian2CUDA 0.27×, PyTorch 0.10×Aligned in choice, independent in code. Their table is why GeNN and not torch. Our PyGeNN kernel (out-degree-banded relays, integer weights for determinism) reaches 1.46× real time on the 3070 Ti in the latched regime. The background-activity term runs on both kernels and agrees between them to counting error on isolated cells and statistically on the network, the two using different random streams.
Vision geometryNeuroMechFly v241 (cited by Fly64 for wide-angle eyes)Loosely aligned. Two ~150° eyes with 15° overlap; hex-axis orientation is an engineered guess, recorded as such.
Panel and robotWLED62 + DDP61; the previous MQTT-era sparkbeest sketch and controllerReused. Servo ports, pulse mapping (1500 ± v·10 µs, left mirrored) and I²C pins taken from the old sketch; transport changed from MQTT to direct UDP with a watchdog. Since 17/09 the controller is an AtomS3R-M12, whose camera also serves as the robot's eyes.

Not used, but read: snedea/flybrain70 (FlyWire, LIF in a browser worker), lixiang1076/fly-brain71, NeuroCraft Fly72 (Minecraft; no code released, “scripted body programs”), the Beat Saber demo (motor patterns recorded, per its author), Jin et al.42, who use the connectome as a trained controller architecture rather than as a dynamical model, a different question, and the State of Brain Emulation Report 202543, which lists a resting-state whole-brain Drosophila model among the open gaps.

9Findings What the measurements say that the sources do not

Fourteen results of this project's own testing that were searched for and not found in the sources consulted: the research library kept for the project (every paper cited on this page and the olfactory, feeding, walking and connectome literature ingested for each experiment), Virtual Fly Brain63, and the web, including the other MaleCNS and FlyWire simulators44,59,60,73,74. "Not found" means not found there; it is not a claim of priority. Unless an entry says otherwise, every result is bounded by the same conditions: one dataset, MaleCNS v1.01, and one build policy (every edge kept, autapses kept); the unit of Figure 5 at 0.31 mV per contact with the live model's signs and thresholds, except where an entry names the settings it ran under, since several of these results are what led to those settings; adaptation and background activity off; stimuli delivered by the experiment harness as a steady depolarisation of a named population rather than as real sensory input, with object sizes in the encoder's frame degrees; one seed on a deterministic kernel, so every number is one exact run rather than an average; readouts of one or two cells where the fly has one or two; and transmitter signs from the connectome's classifier18 against the curated ones. Each entry ends with what a reader with the model would run and compare. Two results already on the page are not repeated here: the graded retina-to-medulla chain not transmitting (Figure 4, the lineage table), which rests on a session measurement rather than a repeatable experiment record, and the left bias of DNa02 (question 6 of section 10), whose sources are unchecked.

1. The antennal lobe amplifies its own resting input, and 67 cells of undecided transmitter carry the excess.

With the connectome's own synapse strengths and every cell of undecided transmitter signed excitatory, the resting firing of the olfactory receptor neurons alone drives the projection neurons to more than ten times their resting rate and the Kenyon cells to their ceiling. The afferent synapse is the fly's; the excess is recurrent excitation inside the lobe, and the 67 local neurons with no transmitter prediction carry it: signed by their class's majority, inhibitory, the lobe rests at the fly's rates.

Measured The antennal-lobe gain experiment: the 2,639 receptor neurons receive random synaptic events calibrated so that an isolated cell fires 8 Hz45, nothing else is driven, 10 s. The antennal lobe reaches its plateau inside 40 to 60 ms and the mushroom body follows within 100 ms; in the last two seconds the receptor neurons fire 21 Hz (the lobe feeds back on them), the projection neurons 78, the local neurons 140, the Kenyon cells 92, APL 399, the whole network 6.1 Hz with about 4,000 cells at or above 100 Hz. The gain from receptor to projection neuron is 9.7 against 0.6 in the fly, from projection neuron to Kenyon cell 1.2 against 0.0226. The graph's receptor-to-projection connection is the fly's rested unitary synapse (14.9 contacts, 4.6 mV, 49 partners per projection neuron against a measured 6.2 mV and about 40 partners35), so 49 afferents at 8 Hz put a projection neuron above threshold by themselves, 9 mV of mean drive against a 7 mV gap; the projection neurons then give each other 21 mV, the local-neuron mass is net inhibitory onto them by only 10,000 mV of 100,000, with 22,700 mV of excitation resting on cells with no predicted transmitter, and each Kenyon cell receives 40 mV from other Kenyon cells against 30 taken by APL. Giving the afferent synapses the short-term depression Kazama and Wilson measured (release probability 0.79, recovery 120 ms derived from their 40 % depression at 7 Hz) halves the excess (projection neurons 59 Hz, Kenyon cells 47, network 4.3, the afferents back at 9.7 Hz), and at Shiu's 0.275 mV keeps the mushroom body dark for 10 s while the lobe holds 54 Hz; no depression setting gives a state between silence and the attractor. The antennal-lobe sign-policy experiment then ran the same input under four policies for the 3,177 cells with no transmitter prediction. Signed by the predicted majority of their annotation class, which negates exactly the 67 antennal-lobe local neurons (their class is predicted 214 inhibitory to 137 excitatory), the lobe rests: projection neurons 4.3 Hz, within the fly's 2 to 5, a receptor-to-projection gain of 0.5 against the fly's 0.6, Kenyon cells 0.4 Hz, the network at 1.6 Hz with only the ring loop of Figure 4 at the ceiling. Silencing the 67 alone gives the same (projection neurons 5.1 Hz); the curated transmitters Virtual Fly Brain holds for 15 of them change little (projection neurons 71 Hz against 78); signing every unpredicted cell zero breaks the feeding pathway (MN9 at 12 % of its plateau, because the unpredicted cells include 268 gustatory afferents). With the Kenyon cells at their measured threshold gap as well, the live model, the Kenyon cells fall to 0.00 Hz.

Bounded by A class statistic applied to types whose own predictions are cholinergic (three of the 19 types, about 4,000 of the 71,600 mV the 67 cells put out); the receptor neurons fall to 1.7 Hz under the class-majority sign, below their 8 Hz, because the local neurons' inhibition of the afferent terminals, presynaptic and divisive in the fly46, is whole-cell inhibition in a point neuron; the lLN1_bc sign (finding 5); one seed of the background events.

Nearest in the literature The synapse and its depression35, the presynaptic lateral inhibition whose strength at rest no source gives46, the all-to-all APL feedback47, and the argument that a fifth of the local neurons may be cholinergic18 are all measured on the fly, not on a model. Shiu et al. start every cell at 0 Hz and report no antennal-lobe rate2; a hemibrain olfactory spiking model keeps its Kenyon cells sparse only with a hand-tuned projection-to-Kenyon scale and homeostatic scaling in the projection neurons48; a MaleCNS GPU simulator records that its glomerular selectivity rested on local interneurons spiking74. The resting gain of a whole-CNS model at the connectome's own weights was not found.

To reproduce Random excitatory events of 4.85 contacts on the olfactory receptor neurons only, 840 per second per cell, all else silent, 10 s at 0.31 mV with lLN1_bc GABAergic and adaptation off; compare the last-two-second rates of the receptor, projection, local, Kenyon and APL populations; repeat with the afferent depression on, at 0.275 mV, and with the 67 unpredicted antennal-lobe local neurons signed inhibitory or silenced.

2. Physiological background input gives silence or the attractor, nothing between.

Spontaneous synaptic input per cell class, at the rates the literature reports, does not give the model a resting state: at the calibrated level it ignites an attractor within a tenth of a second, the olfactory one when undecided transmitters are signed excitatory and the ring loop under the live model, at a little over half that level it is silent, and there is no level in between.

Measured The background-activity experiment: 27 cell classes, each cell its own random train of 1.5 mV events (4.85 contacts, the graph's mean connection) at a rate found on an isolated cell (olfactory receptors 8 Hz, compass neurons 440, other central cells 1 to 5; projection neurons, Kenyon cells, descending and motor neurons nothing, since their rest is inherited26,35), 60 s. At the calibrated table the antennal-lobe / mushroom-body attractor ignites in 60 to 80 ms and holds for 60 s (network 7.1 to 7.6 Hz, 4,149 cells at or above 100 Hz, Kenyon cells 92, projection neurons 77; one of 14 judged classes in range; Fano factor of the population count 11.7; a third of the active cells firing at their refractory interval). At 0.7 of the table the same state after 180 to 220 ms; at 0.5 and below, 23 spikes in 20 s from all 166,700 cells. The receptor events alone ignite the olfactory loop; central events alone ignite the compass ring. On the background the escape response stands out (Giant Fiber 293 Hz), the steering response is marginal (DNa02 44.5 Hz against a 5 Hz background with a standard deviation of 18), and the feeding and small-object responses are lost (MN9 2.5 Hz against 86.5 without the background; no DNp09 spike). With the afferent depression of finding 1 on, two classes are in range instead of one and the same responses are lost. Those runs signed every cell of undecided transmitter excitatory. Under the live model the same background ignites the ring loop of Figure 4 instead, from the start (network 2.8 Hz, 522 cells at or above 100 Hz, the Kenyon cells silent, projection neurons 2.3 Hz, receptor neurons 3.7); the lower levels, run with GLNO signed excitatory, give the loop at 0.7 of the table and silence at 0.5, so there is still no level in between; on the background the escape response still stands out (Giant Fiber 297 Hz), the steering response is marginal (DNa02 31 Hz against a background of 6 with a standard deviation of 20), and the feeding and small-object responses are still lost (MN9 1.5 Hz, no DNp09 spike).

Bounded by Excitatory events only; the lLN1_bc sign (the predicted sign gives the full attractor at 10.3 Hz); per-class adaptation on top turns the plateau into bursts (Fano factor 482); the two kernels agree in distribution, not spike for spike, because they draw different random streams.

Nearest in the literature Shiu et al. assume 0 Hz everywhere2; a resting-state fly model is a named open gap43; the asynchronous irregular state is a balanced-input result from cortex33,49. Fly64 adds background drive and noise to every neuron and reports no resting-state measurement60. A MaleCNS port of the Shiu model reports that a uniform tonic baseline destroys stimulus specificity and abolishes the escape response and names cell-specific spontaneous activity as the missing ingredient73. Li et al. fit a FlyWire rate model to whole-brain calcium recordings and find a sparse set of inhibitory hub neurons sustaining the resting state50, one of them an antennal-lobe local neuron type, lLN2F_b, that sits at the ceiling of this model's olfactory attractor. A spiking whole-CNS model given class-specific resting input, and what it does with it, was not found.

To reproduce The class table at 1.0, 0.7 and 0.5 of its rates, 60 s from silence; compare the late-window rate per class, the count of cells at or above 100 Hz, the population Fano factor, and the four reference pulses (loom, one-eyed motion, sugar, small object) against the same pulses without the background.

3. A pulse into silence ends in one attractor; the second rested on a sign policy and carried the forward drive.

Under the live model every effective input leaves the network in the same self-sustaining state, the ring loop of Figure 4. The antennal-lobe / mushroom-body loop that taste and most lobula pulses ignited while every cell of undecided transmitter was signed excitatory is removed by the class-majority sign of finding 1; the Kenyon cells' measured threshold gap alone keeps the mushroom body out of it but leaves an antennal-lobe loop standing; and that olfactory loop, not the stimulus, carried most of the forward drive the robot's throttle read after a pulse.

Measured The latch, sign, taste, small-object and loom experiments re-run under the live model in runs of 4 to 8 s. Every pulse ends in the ring loop with the Kenyon cells at 0.0 Hz in every 20 ms tick, lLN1_bc at 0 to 2 Hz and APL at 28 to 37 Hz; none releases without adaptation. Each setting alone, on the same pulses: the class-majority sign alone keeps the lobe dark under a sugar pulse (projection neurons 0.25 Hz, Kenyon cells 0.2) and puts the LC9 and LC10c pulses in the ring loop with an excursion of the mushroom body's giant cells (Kenyon cells 3 Hz, APL 258 to 262, DPM 142); the Kenyon-cell gap alone keeps the Kenyon cells at 0.2 to 0.5 Hz but leaves the lobe latched by itself (local neurons of the lLN2 types at 276 to 312 Hz, projection neurons 58, APL 156 to 162, about 1,000 cells at or above 100 Hz, the network at 2.7 Hz). That lobe-only loop holds the leg motor pools at 5.4 Hz and the throttle at 0.20 after an LC9 pulse (93 ticks of forward command), as the joint loop did (5.3 to 5.5 Hz, both wheels on for runs of up to 5.5 s); with the lobe at rest the pools fall to 2.5 to 3.2 Hz, the throttle to 0.09 to 0.14 and the runs to under 0.4 s. Under the build's excitatory sign the joint loop stood at 4.2 Hz with the Kenyon cells at 47 and 2,800 cells above 100 Hz; which pulse landed in which state was not its strength (right-eye LC9, LC10c with LC10d and the calibrated zoom in the ring, bilateral LC9, LC11, LC4 alone and LPLC2 alone in the olfactory loop), and of 53 lobula types driven alone every effective one ignited one or the other within 20 to 200 ms.

Bounded by The common conditions; runs of 4 to 8 s, so a later release is not excluded; the throttle is the decoder's baseline-subtracted leg-pool reading, so the comparison is of the ignition hum, not of walking.

Nearest in the literature DOOMFLY's notes that its network stays busy59, without a characterisation; Shiu et al. and Sapkal et al. run one-second trials from 0 Hz and report no persistent state2,39; the Loihi 2 port of the FlyWire model scaled its synapses down to negligible before adding a background, to prevent runaway excitation44; a MaleCNS port of the Shiu model divides the weight by a synapse-density factor and requires activity to die out after each stimulus, which it does75, the regime this graph enters below about 0.25 mV (finding 4). Which state a pulse ends in, what removes the olfactory one and its share of the motor output were not found.

To reproduce A 0.5 s pulse of 15 mV on T4/T5 subtype a in both eyes, 1 s of 30 mV on the 34 sugar neurons, and 0.5 s of 15 mV on both LC9 populations, in runs of 4 to 8 s from silence, under the four combinations of the unpredicted-transmitter sign (excitatory or class majority) and the Kenyon-cell threshold (7 or 21.5 mV); rank cell types by rate in the second after the pulse and compare the leg pools and the throttle.

4. Shiu's criterion puts the weight on this connectome at 0.30 to 0.32 mV per contact.

The one free parameter of the model, fitted at 0.275 mV on the FlyWire female brain, gives 68 % of maximal MN9 firing on MaleCNS where the criterion asks for about 80 %; the criterion is met between 0.30 and 0.32 mV.

Measured The weight calibration: 0.15 to 0.45 mV per contact, the 34 sugar neurons held at 100, 150, 200 and 300 Hz by a tonic drive re-found at each weight, 2 s, MN9 at 100 Hz against its plateau over the three higher rates. At 0.275 mV MN9 gives 60.5 Hz against a plateau of 89.2 (68 %, or 71 % against the plateau mean); 0.30 gives 77 %, 0.32 94 %, 0.33 101 %, 0.35 85 %; the scatter in the flat top moves the ratio by 10 to 15 points between neighbouring weights, so the answer is a band. Within 30 % of 0.31 the pathway keeps working in Shiu's sense (MN9 rising monotonically from 44 to 118 Hz, the proboscis pool from 26 to 51); below about 0.25 mV the model changes regime (no ignition, MN9 at a tenth of its own plateau). The sweep ran with the predicted signs; repeated at 0.31 with the drives re-found under the live model's signs and threshold, MN9 at 100 Hz is 96 % of its plateau maximum and 97 % of its mean, with the proboscis pool at 41.5 Hz.

Bounded by A regular tonic drive where Shiu used irregular Poisson trains; both hemispheres driven and both MN9 cells read where they drove one side and read the other; a two-cell readout that cannot be averaged over 30 trials; a male whole CNS against a female brain; and the olfactory loop igniting under sugar from 0.25 mV upward, the likeliest reason this graph wants more weight than FlyWire did.

Nearest in the literature Shiu et al. state the criterion and report the model robust to a 30 % change2. An independent MaleCNS port of the same model chose its per-contact value by a sweep against escape and auditory criteria and reports 0.30 and 0.35 passing with 0.25 and 0.40 failing, without a feeding readout, because its build does not type the taste neurons73: consistent with this band, by a different criterion. Another MaleCNS port moves the weight the other way: it divides it by a synapse-density factor, since a male neuron carries a median 1.81 times the synapses of its female counterpart, and passes its own 29 validation cases, sugar to MN9 among them, with activity dying out after each stimulus75. The MN9 criterion itself repeated on MaleCNS was not found.

To reproduce For each candidate weight, find the tonic depolarisation that holds the 34 sugar neurons at 100, 150, 200 and 300 Hz over 2 s; compare MN9's rate at 100 Hz with the largest of the other three.

5. One transmitter call decides whether the antennal lobe can latch on its own.

The self-sustaining olfactory state rests on the sign of thirty antennal-lobe local neurons, lLN1_bc, which the connectome predicts cholinergic and the curated literature calls GABAergic: with the predicted sign their self-excitation lights the lobe whatever sign the undecided cells of finding 1 carry, and with the curated sign the lobe rests under the live model; the sign of the DPM cell, the one the classifier's own paper flags, changes nothing that matters.

Measured The sign scan. In the graph lLN1_bc's 30 cells put +62,985 mV out, +23,888 of it onto themselves and none onto the Kenyon cells; DPM puts +9,019 mV onto 3,989 Kenyon cells; APL puts -53,955 onto 4,063. Under a 1 s sugar pulse at 0.275 mV with the predicted signs the loop stands at 7.27 Hz with the Kenyon cells at 122.6; with DPM flipped it stands (6.17 Hz, Kenyon cells 84); with lLN1_bc flipped it is gone (1.26 Hz, Kenyon cells 0.08, APL 17.5), and with both the same. Under a visual pulse lLN1_bc fires no spike, so its sign cannot matter there, and flipping DPM alone ignites the loop the network otherwise avoids (APL falls to 0, then the Kenyon cells go to 89 Hz). At 0.31 mV the curated sign leaves a sugar loop at half strength (4.28 Hz, Kenyon cells 47, lLN1_bc itself at 158 Hz with every output inhibitory, driven by the loop rather than driving it), and it is what keeps a visual pulse out of the mushroom body, which with the predicted sign at that weight ignites the full attractor 140 ms into the pulse. With the undecided local neurons signed by their class's majority, the predicted lLN1_bc sign still lights the lobe on the receptor neurons' resting input alone (projection neurons 57.5 Hz, Kenyon cells 92 without their threshold gap, the network at 5.4 Hz), where the curated sign leaves it at rest (4.3 Hz); under the live model lLN1_bc fires 0 to 2 Hz after every pulse.

Bounded by The call itself: acetylcholine at a confidence of 0.75 for all 30 cells1, GABA as curated63, with the classifier's paper arguing that lateral excitation is more common than thought and that a subset of lLN1 releases both GABA and glutamate18. The claim is that the model's state rests on the call, not that the call is wrong.

Nearest in the literature Eckstein et al. name DPM as a known misprediction and the antennal-lobe local neurons as a split hemilineage18; the MaleCNS paper gives the classifier's confusion matrix1. The sensitivity of a whole-brain model's resting state to one cell type's sign was not found.

To reproduce The sugar pulse (30 mV, 0.2 to 1.2 s of a 4 s run) at 0.275 and at 0.31 mV, each with lLN1_bc as predicted and as curated; compare the network rate, the Kenyon-cell rate and the count of cells above 100 Hz in the second after the pulse; then the receptor neurons' 8 Hz background for 10 s with the undecided local neurons signed by their class's majority, lLN1_bc each way.

6. High-salt receptor neurons drive the feeding motor neuron as hard as sugar.

Driven at the same level, the 26 high-salt labellar neurons, aversive in the fly, drive MN9 harder than the 34 sugar neurons do, and hold it after the drive, with the olfactory circuit at rest.

Measured The taste-modality experiment: each population at 30 mV for 1 s from silence, with the class-majority sign and the Kenyon-cell gap and GLNO signed excitatory. Sugar: proboscis pool 39.8 Hz, MN9 94.5; water (17 cells): 24.9 and 20.5, a transient (31 Hz in the first half-second, none after); high salt: 41.8 and 107.5, with the left MN9 at 162 and the right at 53; bitter (38 cells): 7.4 and 4.0; the silence control exactly 0. In the 0.3 s after the drive high salt holds MN9 at 103 Hz and the proboscis pool at 43, with the Kenyon cells silent. With GLNO inhibitory, as in the live model, the sugar drive gives MN9 89.5 Hz and falls to 23 Hz within 0.3 s of its end; the high-salt run has not been repeated under that sign. Under the build's excitatory sign for undecided transmitters the same order held (sugar 86.5 Hz of MN9, high salt 107.5), and at 0.275 mV with the predicted signs as well (high salt 57.5 against sugar's 72.5).

Bounded by The common conditions and the modality typing, which is the MaleCNS taste paper's20; the graph encodes connectivity, not valence, and nothing was tuned to suppress it.

Nearest in the literature Shiu et al. report that the second-order cells below the sugar neurons receive high-salt input in the fly19; Tastekin et al. type LB3d as high-salt avoidance neurons and compute effective connectivity from all four labellar subtypes to MN9 and the other feeding motor neurons within a few hops20, a structural measure; the Shiu model was run with sugar, water, bitter and Ir94e neurons, not high salt2; the MaleCNS port that drove all labellar afferents together got no feeding output73; another MaleCNS port reports that six of the neurons in Shiu's female sugar set are LB3d-like, the subtype matched to high-salt lines, and supply about a quarter of the MN9 response there75, the nearest result and one from inside the sugar set. The high-salt population driven on its own was not found.

To reproduce Drive each of the four labellar populations at 30 mV for 1 s in a 1.5 s run from silence; compare the proboscis pool and MN9 during the drive and in the 0.3 s after, and the Kenyon-cell rate after.

7. The pursuit neuron answers a small dark object with two spikes and is then held down by an ascending inhibitor.

Under its documented input, DNp09 fires once per cell at the onset of the object and never again, because the pulse ignites the network and the network's ascending neurons put CL366, DNp09's strongest inhibitor, at its ceiling for the rest of the run.

Measured The LC9 forward experiment and the DNp09 trace. LC9 at 15 mV in both eyes for 0.5 or 2 s: two DNp09 spikes, one per cell, in the first 20 ms tick and none for the rest of the pulse or the 1.5 s after; LC9 itself falls from 50 Hz in the first tick to 5 or 6 Hz; CL366 (two cells, GABAergic as curated and as predicted63) sits at 97 to 118 Hz during the pulse and 102 to 114 after, from the second tick onward; LC9's own feed-forward inhibitors PVLP005 and PVLP020 stay at 2 to 13 Hz. In the graph LC9 is DNp09's largest visual input on both sides (1,130 and 847 contacts), CL366 delivers -47 to -49 mV per spike onto the left cell and -15 to -19 onto the right, and under sustained LC10c drive at 15, 30 or 45 mV DNp09 receives two to three times more inhibition than excitation. DNp09's own output onto the leg pools is 0.8 and 5.8 mV per spike against 166 and 164 for DNa02, so even a sustained DNp09 would need 240 to 510 Hz to move a leg motor neuron directly; the forward command the decoder emits 80 ms after the onset is the ignition of finding 3. Under the live model the same two spikes fall in the same tick. With per-class adaptation on, DNp09 fires through the pulse instead, 12 spikes over its half-second, with CL366 at 41 Hz, off its ceiling, and LC9 holding 14 to 15 Hz; the benchmark, under the live signs, gives 15 (finding 14).

Bounded by A harness pulse, not a real object; the network state that silences DNp09 is the attractor, so a resting state (question 1 of section 10) could change the answer, and adaptation, which weakens that state, already does; one graph.

Nearest in the literature Bidaye et al. find P9 silent during spontaneous walking, an initiator of pursuit, with LC9 as its direct input14; Sapkal et al. show the halting neurons FG and BB inhibiting the descending neurons P9 recruits, in flies and in the Shiu model with P9 stimulated directly39. Neither reports P9 as an onset detector under sensory drive, nor CL366 as its inhibitor; Virtual Fly Brain has a transmitter for CL366 and no function63. Not found.

To reproduce 15 mV on both LC9 populations from 2 to 2.5 s of an 8 s run from silence; compare DNp09's spike times, LC9's rate per 20 ms, CL366's rate during and after, and the leg pools.

8. The haltere afferents are a wing pathway in this graph, and their route to the steering neuron is inhibitory.

The cells the robot's gyro used to drive reach the wing motor pool seventeen times more strongly than the leg pool, have no direct connection to DNa02, and reach the DNa02 on their own side with a net negative sign two synapses out; the engineered input had no wiring behind it.

Measured The proprioception review, on the built graph: per source cell at one hop, a left haltere afferent puts +5.2 mV onto the left wing motor pool and +0.3 onto the left leg pool (unsigned three-hop influence 0.15 against 0.014); no monosynaptic edge onto either DNa02; at two hops -30.2 mV onto the left DNa02 from the left population and -53.1 onto the right from the right, against +2.4 mV per leg chordotonal cell onto its own leg pool at one hop. In the closed-loop experiment the gyro-to-haltere path raised the contralateral DNa02 from 6 to 13 Hz as intended, and with it depressed the leg motor neurons to 1.7 and 1.5 Hz for the second after the stimulus (6.2 and 5.9 without it), never settled (chordotonal cells at 78 to 89 Hz and the command still on at 6 s) and locked the two leg pools together (correlation 0.80 against 0.44). On the floor the assignment that had worked better (83 % direction-following against 67) had been found by trial.

Bounded by A structural measurement under the sign policy, at one and two hops; the influence numbers compare paths from one source, not across sources; the new default has not been re-measured on the floor.

Nearest in the literature The halteres' reflexes act on wing kinematics5, their afferents reach a wing steering motor neuron in the blowfly, and a walking fruit fly holds them still6: the wing route is the physiology. The signed route from the haltere afferents to the walking steering neuron in a connectome was not found.

To reproduce From the built graph, sum the signed weight per source cell from each haltere population onto the wing pools, the leg pools and DNa02 at one and two hops; run the closed-loop experiment with the gyro into the halteres and without.

9. LC10c and LC10d reach DNp09 on the right side only.

In this connectome the courtship-tracking LC10 family reaches the right pursuit neuron and not the left, while the documented input LC9 reaches both.

Measured The DNp09 trace: onto the right DNp09, LC10c 29 cells and 194 contacts, LC10a 16 cells and 65, LC10d 6 cells and 44; onto the left DNp09, none from any LC10 type, and no relay through the anterior optic tubercle carries it; LC9 1,130 contacts onto the left and 847 onto the right. Virtual Fly Brain's male connectome gives the same picture: 18 LC10c cells onto the right DNp09 at 5 to 13 synapses each and none onto the left, LC9 onto both63. In the harness a right-eye LC10c pulse gives DNp09 as many onset spikes (two) as a bilateral one.

Bounded by One male, its proofreading, and connections of 5 to 13 synapses per cell, where a single missed or false edge counts; a build at the connectome's 0.5 confidence threshold.

Nearest in the literature Bidaye et al. found that LC10 activation did not drive P9 and that LC9 does14; the LC10 family tracks the courtship target through LC10a and the anterior optic tubercle12; the MaleCNS paper reports LC10a isomorphic between the sexes, its targets dimorphic, and the optic-lobe connections of the male strongly bilateral1. A one-sided LC10 input to DNp09 was not found.

To reproduce From the public MaleCNS synapse table, list DNp09's presynaptic cells per side and group them by type; drive LC10c on one side, then the other, at 15 mV.

10. MN9 answers a sugar drive unequally, whichever side is driven.

With the sugar neurons of both sides driven alike, the left MN9 fires three to seven times the right, at every weight and sign tried; driven from one side only, the left cell still dominates.

Measured The taste-modality experiment and the sign scan, from per-cell counts: during 1 s of bilateral sugar drive the left and right MN9 fire 142 and 37 Hz under the live model, 150 and 39 with GLNO excitatory, 135 and 38 under the build's excitatory sign for undecided transmitters, 128 and 17 at 0.275 mV with the predicted signs, 139 and 31 with the curated sign at 0.275, 149 and 35 with the predicted sign at 0.31; under high salt 162 and 53, water 113 and 29, bitter 12 and 0; in the calibration protocol at 100 Hz, 109 and 12. The ratio narrows from 7.5 to 3.6 as the weight rises because the right cell gains more. The feeding experiment of Shiu's validation (finding 12) then drove one side at a time at 100 Hz: the right sugar neurons give the left and right MN9 148 and 38 Hz, the contralateral cell ahead as Shiu found, and the left sugar neurons 143 and 44, the ipsilateral cell ahead; bitter or Ir94e neurons driven on one side drive the left MN9 to 138 Hz where driving both sides gives 1.5 to 4.5 Hz.

Bounded by Two cells in a deterministic model; a tonic drive where Shiu used Poisson trains; one graph.

Nearest in the literature Shiu et al. report unilateral sugar input driving the contralateral MN9 harder than the ipsilateral one2; Tastekin et al. describe the feeding motor neurons as one or two left-right pairs20. An MN9 pair whose left cell dominates whichever side is driven was not found; whether it is the male's wiring, its proofreading or a sign is open.

To reproduce 30 mV on the 34 sugar neurons for 1 s from silence; compare the two MN9 cells' rates during and after; then hold one side's sugar neurons at 100 Hz, then the other's, and read both cells.

11. Signed as fast excitatory transmitters, the dopaminergic cells drive the mushroom body, and silencing them does not quiet the Kenyon cells.

Shiu's policy signs the dopaminergic cells excitatory at the model's 5 ms synapse. With the antennal lobe lit, their output is what brings the Kenyon cells into the olfactory loop; with the lobe at rest and the Kenyon cells at their measured gap, it is what holds APL, DPM and the output neurons above silence; and removing it makes the Kenyon cells busier rather than quieter.

Measured The mushroom-body rest experiment: the receptor neurons on their 8 Hz background, nothing else driven, 10 s, with the output of the 392 dopamine cells set to zero or left as signed, rates over the last 2 s. In the graph the dopaminergic cells put +17 mV onto each Kenyon cell per volley (32 partners at 0.54 mV), +1,255 mV onto APL and +127 onto each output neuron. With the undecided transmitters signed excitatory and the lobe at its plateau, removing them takes the Kenyon cells from 92 to 8.2 Hz and APL from 399 to 310. With the class-majority sign and the Kenyon cells at the model's 7 mV gap, it takes the Kenyon cells from 0.4 to 1.1 Hz (184 to 348 of 4,064 active), with APL at 67 to 71 Hz, DPM from 46.5 to 33 and the output neurons from 24 to 13; silencing all 541 monoaminergic cells gives the same picture. With the Kenyon cells at Turner's gap as well, as in the live model, and silent, removing them takes APL from 36 Hz to 0, DPM from 42.5 to 0.2 and the output neurons from 21 to 9.

Bounded by A fast excitatory synapse is the policy's form, not the fly's; the fly's resting rates of APL, DPM and the output neurons are not in the sources consulted, so the numbers above set no target; why the Kenyon cells rise is not isolated (APL's rate holds, while the lobe shifts at the same time, projection neurons 4.3 to 2.3 Hz and receptor neurons 1.7 to 3.6); one seed.

Nearest in the literature In the fly the dopaminergic cells act on the Kenyon-cell to output-neuron synapses of their own compartment through D1-like receptors51, depress them when paired with odour52, act through distinct receptor pathways with distinct timing53, and evoke in an output neuron a slow depolarisation that peaks more than 2 s after a brief stimulation, largely by volume release54. Shiu et al. assign the monoamines to the excitatory category and note that they are modelled less well2; a MaleCNS port gives them no fast effect at all75. What the fast-excitatory policy does to the mushroom body of a whole-CNS model at rest was not found.

To reproduce The receptor neurons' 8 Hz background for 10 s under the four combinations of the unpredicted-transmitter sign (excitatory or class majority) and the Kenyon-cell threshold (7 or 21.5 mV), each with the dopamine cells' output at zero and as signed; compare the Kenyon cells, APL, DPM and the output neurons over the last 2 s.

12. Shiu's validation carries to a second animal's connectome at three quarters, with nothing tuned, and falls short where single cells are silenced.

Carried to the male connectome by cell type and run as Shiu ran it, 120 of the 164 experimental predictions his FlyWire model was scored on hold here, against his 150, and the benchmark, scoring the same runs under a protocol adopted for adapting cells, counts 130: the whole-circuit predictions hold, and the shortfall is in which single cells the feeding response needs and which cells extend the proboscis on their own.

Measured The feeding, activation, silencing and grooming experiments and their scorecard, under the live model, each driven cell held at its target rate by a tonic drive, one deterministic run per row, each run recording the full model it was built with. Cell types were carried over through the FlyWire type annotations: 99 of Shiu's 106 feeding types map (33 by Shiu's own name in the male connectome's synonyms, 66 through a FlyWire type), 7 have no cells here, and 120 of 164 predictions hold (77 % of the 156 the graph can be asked). The whole-circuit rows hold: 12 of 14 sugar-responsive cells, 8 of 10 water-responsive cells, all four bitter and Ir94e suppression predictions (on sugar at 100 Hz, bitter or Ir94e at 100 Hz take MN9 from 93.5 Hz to 4.5 and 1.5; at 200 Hz bitter eliminates it and Ir94e leaves 15 %), the three antennal mechanosensor responses, and silencing the grooming interneuron aBN1 takes both grooming descending neurons to 0. The shortfall: 3 of 10 sugar-required and 6 of 11 water-required cells (Shiu 6 and 10), because silencing any one second-order sugar cell moves MN9 by 0 to 10 % here and the premotor Roundup by 52 %, where his model had Clavicle and Zorro carrying 33 and 23 %; and 79 of 106 sufficiency rows (Shiu 101), five of the 20 misses being one to three spikes. The benchmark now scores Shiu's rows under a protocol adopted so that a model with adaptation can be scored on the same terms: the receptor-neuron rows hold the driven cells at their target rate, the sufficiency rows keep the drive found on the model without adaptation, and a sufficiency row counts MN9 as driven at 5 % of the model's own strong-sugar response instead of above 0 Hz. Under it the live model scores 130 of 164, with 89 of the 106 sufficiency rows; this is a change of scoring, not of the model, and the count under Shiu's own criterion, 120, stands beside it. Under a water drive the proboscis motor program is all or none within the second: MN9 and about 30 gnathal cell types either fire to the end of the drive or stop together within 100 ms, so a water-silencing row records whether the silencing changed that switch, not the drive the cell carries; read against the program while it runs, only Roundup's silencing lowers MN9. Signing GLNO inhibitory moved ten rows against the run before it (124 of 164): G2N-1's requirement, which sat on the 20 % line; four sufficiency rows that now draw one to eight spikes of MN9, with the GLNO module of the ring loop no longer igniting during the drive; four water rows through the switch; and the second grooming interneuron's requirement, which rests on a drop-out of the descending neuron while the first interneuron still fires, not on its own input. Under per-class adaptation, with every row run under that protocol, 125 hold (123 under Shiu's own criterion), the bitter and Ir94e suppression at 100 Hz lost (finding 14).

Bounded by Typing across connectomes (66 types matched only through a FlyWire type; the aBN2 grooming interneuron a structural candidate); a male whole CNS against a female brain; both sides driven with a tonic drive and one run per row where Shiu drove one side with Poisson trains over 30 trials; the weight at 0.31, which puts MN9 on a plateau from 50 Hz of sugar input and leaves little room for a single silenced cell to show; the water rows read a switch whose trigger is not identified.

Nearest in the literature Shiu et al. score their own model at 150 of 164 and report 90 to 95 % of its predictions unchanged at weights 30 % lower or higher2. Two MaleCNS ports hold the Shiu model to criteria of their own: 29 cases including sugar to MN9 and bitter leaving it silent, passed at a weight divided by a synapse-density factor75, and escape and auditory criteria for the weight sweep73. The 164-prediction table run on a second connectome was not found.

To reproduce Map Shiu's FlyWire cells to male types through the FlyWire type annotations; for each row of his prediction table drive the named population at his rate, or silence the named cell by zeroing its outputs, and read MN9, the named feeding cells or the grooming descending neurons against his criteria.

13. What stays latched is a set of reciprocal pairs, each able to re-fire its partner with one or two spikes.

The ring loop of Figure 4 is five independent modules, each held by excitation between a few cells strong enough that one or two spikes of a partner carry a cell back over threshold. Nothing in a current-based unit without adaptation stops such a pair below its refractory ceiling; the octopaminergic cells that fire first hold nothing, the inhibitory members are a third of the excitation they face, and two of the five modules exist only because a policy signs an undecided cell excitatory.

Measured The ring-loop experiment (115 conditions, 4 s, visual and sugar pulses) and the graph. One spike through c contacts is a jump of 0.31 c mV that peaks at 0.157 of its size, so a connection of 144 contacts carries its target over the 7 mV gap alone; 69 excitatory reciprocal pairs in the graph clear that bar in both directions and 493 clear half of it. The modules: the two DNg33 cells, 773 and 723 contacts each way, one spike a 35 mV peak, still firing 85 to 94 Hz from their other inputs with their own outputs silenced; FR1, 18 fan-shaped-body cells all-to-all within each side, which give each other 125 of their 131 mV of drive; the compass's GLNO and PEN_a cells, +152 and +186 mV per volley onto each other against 79 and 32 from their glutamatergic partners ExR6 and ExR4; GNG016 with the taste afferents it contacts; and the abdominal pair INXXX137 and INXXX181. Silencing the octopaminergic cells that ignite first, or setting all 101 octopamine cells' output to zero, leaves every module after a visual pulse; silencing the inhibitory members raises the loop (ExR6 and ExR4 together, 440 to 580 cells at or above 100 Hz). GLNO at its measured inhibitory sign removes the compass module, GNG016 at the GABA sign Virtual Fly Brain's class carries removes the taste module after a visual pulse; no sourced sign releases the rest; per-class adaptation releases every module after both pulses in the benchmark, the DNg33 pair falling from about 120 Hz in the second after a visual pulse to 78 Hz in the last second of the run (finding 14).

Bounded by The one-spike criterion ignores summation; 4 s runs; no published threshold, time constant or rate exists for GLNO, the DNg33 pair, FR1 or the gnathal and nerve-cord types; the GLNO measurement is a population functional-connectivity result, possibly polysynaptic, by its authors' caveat21.

Nearest in the literature In a spiking model of the protocerebral bridge built from light-microscopy connectivity, strengthening excitation or weakening inhibition drives the compass ring into circuit-wide seizure55: the compass module's mechanism, in one circuit with weights set by hand, where here it is one of several modules at the connectome's own weights. Li et al. find a fitted FlyWire rate model's resting state held by inhibitory hubs locked to excitatory partners, whose silencing releases the partner into runaway activity50; here the inhibitory members exist but are a third of what they face, and silencing them raises the loop. The fly's compass holds a bump with its PEN1 cells at about 4 Hz40, where the model with GLNO excitatory holds the whole ring at 200 Hz. A census of self-sustaining reciprocal pairs in a whole-CNS spiking model was not found.

To reproduce From the built graph, list the excitatory reciprocal pairs whose single-spike peak (0.157 times 0.31 mV times the contact count) clears the 7 mV gap each way; run the T4/T5 and sugar pulses, silence each module's members in turn, and repeat with GLNO and GNG016 at their inhibitory signs and with per-class adaptation.

14. Adaptation by cell class ends the latch and the ceiling, and costs the feeding and escape responses.

With the per-class adaptation of section 5 switched on, the benchmark finds no module of the ring loop latched after either test pulse and almost no cell at the refractory ceiling on the calibrated background; the compass neurons rest inside their measured band, more cell classes sit near their target rates, and the two eyes' motion responses mirror each other. The price is the feeding motor neuron at about a third of its rate at every sugar level, a lower Giant Fiber response to looming, and a population that bursts rather than rests. It is the model's main candidate for a resting state, not the live model.

Measured The benchmark, the project's fixed battery of Shiu's validation, the four validated responses and the resting-state measures, run with adaptation off, as in the live model, and on, everything else equal. The background of the resting-state conditions specifies each class's resting rate; an adapting isolated cell fires below its target on the event rates derived for a non-adapting one, so those rates are re-derived for the adapting cell by the same procedure, never fitted to the network. The pulses run without a background. One seed, and three seeds for the background conditions. After a 0.5 s visual pulse and a 1 s sugar pulse, no module is latched in the last second, against three and four of five without adaptation, and no cell is at or above 100 Hz, against 304 and 391; the network's mean rate falls below 0.1 Hz for good 2.8 and 1.5 s after the pulse, where without adaptation it never does within the run; after the visual pulse the DNg33 pair, at about 120 Hz in the second after it, is at 78 Hz in the last. On the calibrated background 2, 2 and 4 cells sit at the ceiling at the three seeds, against 543, 450 and 476, and one, one and two modules are latched, against four, three and three: the ones left are two-cell modules just over the 100 Hz line (the GNG016 pair at 112 Hz); on the olfactory receptor neurons' background alone, none at the first seed and at most two cells and one module at the others. The P-EN1 compass neurons rest at 4.9, 5.8 and 4.9 Hz, inside the band of 1.3 to 6.5 Hz that loose-patch recordings give for standing flies40, against 0.35, 0.25 and 0.33 Hz without adaptation, and 8 of the 14 background classes with a target rate are within a factor of two of it, against 2 or 3. Motion in one eye now gives mirrored steering: DNa02 left minus right is +18 Hz for the left eye and -23.5 for the right, against +30.5 and +2.5 without adaptation, and symmetric motion gives -9 Hz against +40.5 (question 6). DNp09 fires 15 spikes through the LC9 pulse, against 2 (finding 7). The costs: with the sugar receptor neurons held at 100, 150, 200 and 300 Hz MN9 fires 34, 25, 28 and 34 Hz, against 95 to 99, so Shiu's criterion2, MN9 at 100 Hz as a fraction of its maximum at the higher rates, passes (0.99, against 0.96) only as a ratio on a flat, smaller response; at the drives that give 100 Hz without adaptation, where the adapting receptor neurons reach 60 Hz, the ratio is 0.47. The Giant Fiber fires 232 Hz to the calibrated zoom, against 302, and the other looming descending neurons a fifth to a third less. On the background the Fano factor of the population's spike count per 20 ms tick is 261, 261 and 227, against 46, 31 and 27: activity comes in network-wide bursts, where an asynchronous irregular rest would sit near 1. Shiu's validation holds at 125 of 164, against 130 (123 against 120 under his own MN9 criterion), with bitter and Ir94e at 100 Hz no longer suppressing the sugar response.

Bounded by The adaptation strengths are measured for some classes and inferred by class for the rest (section 5). Vision enters at the direction-selective and lobula cells, bypassing the photoreceptors, lamina and medulla, whose own adaptation the fly has, so the visual responses are judged without the periphery. Shiu's protocol was built for a model without adaptation even where its verdicts are fly experiments; the benchmark now holds the receptor neurons at their rate, drives the sufficiency rows at the non-adapting model's drive and counts MN9 against the model's own strong-sugar response (finding 12), and the gate's ratio does not ask for the response's size. One seed for everything but the backgrounds; runs of 4 to 20 s. These costs are why it stays a candidate: two validated responses are partly lost, the population bursts, and the visual periphery is not yet in the loop to be judged with it.

Nearest in the literature Shiu's model is memoryless and starts every cell at 0 Hz2. A MaleCNS port of it carried an adaptive threshold, a standard spike-frequency adaptation not taken from fly measurements, to stop the whole CNS running on after a strong stimulus, and dropped it once a weight scaled by synapse density kept the network stable without it75; here the weight is held by Shiu's criterion without adaptation (finding 4) and adaptation is set per class. A FlyWire rate model fitted to whole-brain recordings rests through inhibitory hubs rather than adaptation50, and the asynchronous irregular state the Fano factor tests for is a balanced-input result49. What per-class adaptation does to the resting state and the validated responses of a whole-CNS spiking model was not found.

To reproduce The benchmark's battery with per-class adaptation off and on, the background's event rates re-derived for each arm's isolated cell and Shiu's rows under the same drive protocol in both; compare the cells at or above 100 Hz and the modules latched after the pulses and on the background, the release times, P-EN1's resting rate, DNa02 under motion in each eye, MN9 with the sugar neurons held at 100 to 300 Hz, the Giant Fiber during the zoom and the population Fano factor, with the backgrounds at three seeds.

10Open questions Model and robot

What the experiments so far leave unresolved. Each entry says what was measured, what the literature says, what the model and the robot are expected to do if the answer goes one way or the other, and what would settle it.

1. What holds the ring loop, and what would give the model a resting state?

Without adaptation every input tried is a pulse into silence that ends in the ring loop (Figure 4): under the live model four small modules, each a group of cells that re-fire each other with one or two spikes and sit at the refractory ceiling (finding 13), with the antennal lobe and the mushroom body at rest once the undecided local neurons carry their class's sign and the Kenyon cells their measured gap (findings 1 and 3). The fly has no such state: its compass holds a bump with the PEN1 cells at about 4 Hz40, and a resting-state fly model is a named open gap, mean rate bounded near 4 Hz43. Background input at the literature's spontaneous rates does not help: it ignites the loop or leaves the model silent, with no level in between, and on it the feeding and small-object responses are lost (finding 2). A fitted rate model of the FlyWire brain rests because inhibitory hubs hold their excitatory partners in check50; here the inhibitory partners exist and carry a third of the excitation they face. Two of the remaining members carry an inhibitory class transmitter in Virtual Fly Brain where the model signs them excitatory by policy, GNG016 GABA and AN27X013 glutamate; both class entries are other connectomes' predictions written into the ontology rather than measurements18,63. Measured, GNG016 at GABA removes the taste module after a visual pulse and AN27X013 at glutamate changes nothing, so neither is adopted, and the disagreement stays recorded here. What would settle the question, in order: a measured transmitter for GNG016; a sourced intrinsic or synaptic property for the DNg33 pair and FR1, whose members are cholinergic and whose reciprocal connections are strong enough that no transmitter question applies; and the inhibitory share of background activity, since the term tried is excitation only49. If a sourced property ends the modules, the background is re-run and the resting state measured against the fly's rates class by class. Per-class adaptation already ends them on the benchmark after both test pulses, and on the background leaves only one or two two-cell modules just over the ceiling line, with the compass neurons inside their measured band (finding 14); it is the candidate the resting state is now built from, and what it costs is question 5. On the robot, the forward motion after a pulse is the ignition hum read by the throttle, and it halved when the olfactory loop went (finding 3); a resting state would leave the beest still until a real pursuit signal exists.

2. Why does single-cell silencing fall short of Shiu's validation?

Carried to this connectome by cell type, Shiu's validation holds at 120 of his 164 predictions under his own criterion, 130 as the benchmark now scores it, a change of scoring rather than of the model (finding 12). The whole-circuit predictions hold; what falls short is silencing, 3 of 10 sugar-required and 6 of 11 water-required cells against his model's 6 and 10, because silencing any one second-order sugar cell moves MN9 by a few per cent here where his model moved it by a fifth to a third2,19. The water count reads something else: under a water drive the proboscis program stops all at once partway through the drive or not at all, and the silencing rows record that switch (finding 12); what triggers it is open. For the sugar rows two explanations are open. The male graph may spread the sugar drive over more second-order cells, so that no single one is needed; or the calibrated weight, 13 % above his, with a drive on both sides, puts MN9 on a plateau from 50 Hz of input, where any single cell's share is hidden. They predict different things: spread drive survives a lower weight and a one-sided drive, a plateau does not. What would settle it is the same silencing rows at Shiu's 0.275 mV and under a one-sided drive, read on the contralateral MN9 as he read it, then the sufficiency misses of one to three spikes with Poisson input over repeated trials. The weight itself holds under the live model (MN9 at 96 % of its plateau at 100 Hz, Figure 5), and the scorecard is the reference every later change to the model is re-measured against.

3. Is DNp09 ever tonic under realistic input, and is BPN the route to sustained walking?

A small dark object encoded into LC9 reaches DNp09 as two spikes in the first 20 ms and nothing more, under a 0.5 s pulse or a 2 s one, unless adaptation is on (finding 7): LC9 itself falls from 50 to 5 or 6 Hz within one tick, the ascending-neuron-driven inhibitor CL366 sits at its refractory ceiling (100 to 120 Hz) from the second tick to the end of the run, and the forward command the decoder emits 80 ms after the onset is the network's own ignition, not the target. The DNp09 trace found LC9 its largest visual input on both sides, LC10 reaching only the right cell, and its output onto the leg pools at 0.8 and 5.8 mV per spike against 166 and 164 for DNa02. In vivo, P9 (DNp09) is silent during spontaneous walking and initiates courtship pursuit with an ipsilateral turn, while the bolt protocerebral neurons, higher-brain cells rather than descending neurons, drive straight fast walking and scale with speed14. Pursuit walking on the robot appears only if some cell fires tonically into the legs; otherwise the leg pools move only with the hum. BPN is typed in MaleCNS as CL210_a, SMP459, SMP460 and SMP461, and its four output descending neurons (DNge053, DNg100, DNg55 and DNge050, of which DNg100 is already a readout population) are in the graph39,63; the next step is to drive them.

4. What do the detectors see in real degrees?

LC9 prefers dark objects of about 4.5 by 2 degrees and LC10a 15 to 30 degrees12; LC11 is tuned to moving objects of about 4.5 to 5 degrees and answers a bar much less8,12,56,63; the LC types form an ordered size map15. The encoder works in frame degrees at about five times that scale (a 55 degree webcam spread over two 150 degree eyes), with no ommatidial blur and absolute rather than adapted contrast, so the phone-sized target that drives LC10 today is about 7 real degrees and may belong in LC9's band. On the robot's 120 degree camera the size bands, thresholds and false-positive rate all change; a recalibration (real field, 5 degree blur, Michelson contrast, size gates from the literature) follows, after which the small-target experiments are re-run as they stand.

5. What would remove the costs of adaptation?

Per-class adaptation from the physiology where it exists and from class inference elsewhere17,27,29,31,34 ends the ring loop and the ceiling on the benchmark and is the main candidate for the resting state (finding 14); what stays open is its cost. Measured there against the live model: MN9 at about a third of its rate at every sugar level (34 Hz against 95 with the sugar neurons at 100 Hz), the Giant Fiber lower to looming (232 Hz against 302), Shiu's validation at 125 of 164 against 130 with the bitter and Ir94e suppression at 100 Hz lost, and a population that bursts rather than idles on the background (Fano factor 227 to 261 against 27 to 46). The feeding gate is the sharpest of these. Shiu's criterion2 passes under adaptation only as a ratio on a flat, smaller response (0.99), and it was set on a model without adaptation, as was the rest of his protocol (50 Hz activation, receptor neurons at 100 Hz, MN9 above 0 Hz), even where its verdicts are fly experiments; the benchmark now holds the receptor neurons at their measured rate and counts MN9 against the model's own strong-sugar response, and no measure yet asks for the size of MN9's response as well as its shape. Two further limits bound the candidate: vision enters at the direction-selective and lobula cells, bypassing the photoreceptors, lamina and medulla, whose own adaptation the fly has, so the visual responses are judged without the periphery; and the adaptation strengths of several classes are inferred rather than measured. What would settle it: a sourced property of cells or synapses (a synapse class for the monoamines, transmitter corrections, synapse strength by size, an inhibitory share of the background) that restores MN9 and the Giant Fiber while keeping the release, each measured on the benchmark with adaptation off and on; the visual periphery in the loop; and the Fano factor brought toward 1. On the desk adaptation roughly halves the motor output (leg pools 2.5 Hz against 5); live runs keep it off until the benchmark shows it helps without losing a validated response.

6. Is the left bias of DNa02 the fly's, or the graph's, and what sets the steering on the floor?

Under symmetric T4/T5 drive DNa02 fires 35 Hz on the left and 4 on the right in the DNa02 asymmetry experiment, and 53.5 and 13 in the benchmark under the live model; the experiment traced the extra inhibition to four cells two synapses upstream (LAL046, LAL083, LAL120_a, GNG515), and through LLPC1 the right cell reaches 122 Hz, so the deficit belongs to the T4/T5 route. The four sources are unchecked. Synaptic signs come from the MaleCNS transmitter classifier, whose accuracy on this dataset is the neuron-level confusion matrix in its own paper1; classifiers of this family confuse glutamate with GABA most often and mispredict some cell types outright18, and the wiring is a single male1. If one is mis-signed or under-proofread, the robot's left bias is an artefact the readout should not compensate for; if they hold, it is this fly's wiring. With per-class adaptation on, the left bias goes, turning into a small excess on the right (23.5 Hz on the left against 32.5) and motion in one eye gives the mirror of motion in the other (finding 14), so the latched network, not only the wiring, may carry part of it. The check is their transmitter confidence and proofreading status, then ablating them in the harness. On the floor the question has a second half. The first run with each eye seeing its own side (section 3) turned the beest about 45 degrees one way, back, and back again for 90 s, where every earlier floor run, with the eyes swapped, had rotated it in one direction: 56 reversals of the commanded turn, heading swings of 8 degrees in the median and up to 38, a net heading change of 21 degrees, with DNa02 at 51 Hz on the left and 68 on the right and 4.9 frames a second arriving from the camera. With the eyes swapped a turn drove the stimulus further into the wrong eye and the turn continued; with them correct the loop corrects itself and overshoots, the turn command near its limit (180 of 200) carrying the body past the stimulus. Whether that overshoot is the readout (the turn gain and the 0.15 s steering time constant), the frame rate, or the left-right balance of the model itself is what the next floor runs, with the turn gain varied and the frame rate logged, would separate.

7. Do single LC channels ever add up to pursuit?

The encoder drives each LC type from its own detector, and every single-channel pulse ends the same way: an onset burst or nothing, then the attractor; LC9 and LC10c together give the strongest DNp09 onset (three spikes), the strongest DNa02 (28 Hz left against 2 right) and the one turn command seen from a single object, and drive the legs as much as LC9 alone. In the fly the LC types share inputs, converge on shared targets and act as a population: a knockout-trained model of male courtship needs combinations of LC types for every behaviour and loses little when any one is silenced13, and stimulus identity cannot be read from one glomerulus alone56. Single-channel pulses may never produce the fly's pursuit; a combined, timed input from one target might. The test is to drive LC9, LC10 and LC11 as one fly-sized target would, at three distances, against the single-channel runs.

Glossary

Connectome
A complete wiring diagram: every neuron and every synapse between them, reconstructed from electron-microscope images. MaleCNS is the first for a whole adult male fly nervous system1.
LIF neuron
Leaky integrate-and-fire: the simplest spiking neuron model: a voltage that leaks, sums input, and fires when it crosses a threshold. Figure 5.
Optic lobe
The visual processing stack behind each eye: lamina, medulla, lobula and lobula plate. 95,000 of the fly's neurons live here.
Ommatidium · eye column
One facet of the compound eye: a lens, eight photoreceptors, and the column of neurons behind it that keeps processing that one point of the scene. Each eye has ~800 of them, arranged in a hexagonal lattice ~5° apart, the "hex columns" in the data. The fly's image is 800 pixels per eye; everything downstream works column by column with neighbours.
R1–R6 photoreceptors
The six outer photoreceptors of each ommatidium: broadband, high-sensitivity, fast. They do the fly's luminance and motion vision (R7/R8 in the centre handle colour and UV). They respond to light with a graded voltage, not spikes, and release histamine, which inhibits their targets, so more light means less transmitter. Their job is to convert light to a fast analogue signal and to adapt to the mean brightness so the next stage sees contrast, not absolute level. Not used here: in a spiking model a graded, inhibitory-only cell transmits nothing useful; we measured L1 responding with the correct sign and nothing beyond it.
Lamina · L1, L2, L3
The first neuropil: one cartridge per column, receiving the six R1–R6 that look at the same point in space. L1 and L2 invert and split the signal into an ON channel (L1: brightening) and an OFF channel (L2: darkening), each reporting change in brightness rather than brightness itself, with lateral inhibition between neighbouring cartridges that sharpens edges. L3 carries a slower, sustained luminance signal. In engineering terms the lamina is a high-pass filter and a contrast normaliser with an ON/OFF split, the same first steps as a camera pipeline's auto-exposure and edge enhancement. Not used here: it works as far as it goes (our L1 rates fall on the lit side as they should) but the layer after it does not carry the signal on.
Medulla · Mi1, Tm1, Tm2, Dm
The second and largest neuropil (~40,000 neurons per eye, in ten layers). It takes the ON and OFF channels and builds the inputs the motion detectors need: Mi1 and Tm3 carry ON signals with different delays, Tm1, Tm2 and Tm4 carry OFF signals with different delays, and the Dm and Pm amacrine cells (glutamatergic and GABAergic, spanning many columns) provide the lateral inhibition that adapts gain and suppresses uniform illumination. Delaying one copy of the signal relative to a neighbouring column is the trick that lets T4/T5 compute direction: a moving edge arrives at column A first and column B a moment later, and the detector fires when the delayed A signal coincides with the fresh B signal (a Hassenstein–Reichardt / Barlow–Levick correlator). Not used here: in the spiking model the Dm amacrines lock at 40–100 Hz and clamp the columnar cells (Tm1 at 0.4 Hz), so no delayed-and-compared signal reaches T4/T5, which is why the encoder computes the flow itself and injects at T4/T5.
Lobula · lobula plate
The third and fourth neuropils. The lobula plate holds the T4/T5 terminals in four direction-specific layers and the wide-field tangential cells (HS, VS) that sum them into whole-eye optic-flow signals (yaw, pitch, roll) used for course control. The lobula holds the feature detectors: the LC and LPLC columnar neurons (LC4/LPLC2 for looming9, LC11 for small objects15,56, LC10 for courtship targets12,13) that project to specific glomeruli in the central brain8,11. These are the first stages that work in the spiking model, which is why they are the injection points.
T4 / T5
The fly's elementary motion detectors in the lobula plate: T4 for brightening edges, T5 for darkening ones, each in four subtypes tuned to the four directions7.
LC4, LPLC2
Lobula columnar and lobula-plate/lobula columnar neurons that respond to looming (approaching) objects and drive the escape circuit; LC4 reports the object's angular velocity and LPLC2 its angular size, and the Giant Fiber sums the two9,10.
Descending neurons
The ~1,300 cells whose axons carry commands from the brain into the ventral nerve cord. DNa01 and DNa02 steer63, DNp09 walks forward14, MDN walks backward36, DNp01 (the Giant Fiber) triggers escape9.
Giant Fiber
A single very large descending neuron per side whose spike triggers the escape jump within milliseconds.
Ventral nerve cord
The insect's spinal-cord equivalent: the leg, wing and abdominal circuits and all 815 motor neurons.
Halteres
Two tiny club-shaped organs behind the wings, the remains of a second pair of wings. In flight they beat with the wings and the fly reads the forces on them to hold itself steady5. On foot the fruit fly leaves them alone6, so nothing in the rig drives them.
Chordotonal organ
Stretch-sensing organ at a joint. Inside each femur a fan of about 135 sensory cells reports where the joint is, which way it is moving and how fast4. The beest's actual servo speed drives them.
Johnston's organ
The mechanosensory organ in the antenna: a ring of several hundred stretch-sensing cells at the base of each antenna reads how its feather-like arista is bent. Some of them hear (courtship song); others answer a steady pull, which is what gravity and a steady breeze both feel like (JO-C/E)3,57. The beest's tilt drives the latter.
Antennal lobe · Mushroom body
The fly's olfactory processing centre and its learning/memory centre. In this model they rest under every pulse once the antennal lobe's undecided local neurons carry their class's inhibitory sign and the Kenyon cells their measured threshold; with every undecided cell signed excitatory they latch together after a taste pulse and after most lobula pulses (Figure 4, findings 1 and 3).
Kenyon cell
The intrinsic neuron of the mushroom body, about 2,000 per side in the fly and 4,064 in this connectome. Each receives a handful of projection-neuron inputs and fires only when many coincide: Turner, Bazhenov and Laurent measured 21.5 mV from rest to spike threshold, 1.4 mV per input, so about 15 inputs at once, and a resting rate near 0.1 Hz with a fifth of the cells silent26. The model gives these cells that threshold and no other cell type a threshold of its own (Figure 5).
Central complex
The fly's navigation centre, whose compass neurons hold the heading as a bump of activity on a ring and integrate angular velocity into it40. The EPG cells carry the bump, the PEN cells shift it, and GLNO brings input into the noduli onto the PEN cells, inhibitory as measured21,22.
Ring loop
The state every pulse into silence leaves the model in, named for the compass ring of the central complex: a set of small self-sustaining modules, each a group of cells whose connections to each other are strong enough that one or two spikes of a partner re-fire a cell, holding each other at the refractory ceiling. Under the live model four modules: the DNg33 pair, FR1 of the fan-shaped body, a gnathal taste cluster around GNG016 and, after a sugar pulse, an abdominal pair of the nerve cord; the compass ring itself holds a sparse pattern at about 6 Hz. With GLNO signed excitatory a fifth module, GLNO with the PEN cells, holds the whole compass ring at 200 Hz (Figure 4, finding 13).
Labellar taste neurons · MN9
The gustatory receptor neurons in the labellum, the tip of the proboscis, typed in the male connectome by modality: 34 sugar, 17 water, 26 high-salt and 38 bitter cells20. MN9 is the motor neuron that protracts the rostrum, the proboscis-extension readout of the Shiu experiments2,19.
Fano factor
The variance of a count divided by its mean, here the number of network spikes per 20 ms tick over a run. Cells firing independently at a steady rate give 1; a value below 1 means clockwork regularity, a value well above 1 means bursts or a drifting rate. It is the test used for a resting state that is irregular rather than latched or bursting: the background-noise run with adaptation off gave 11.7 (a steady attractor), the same run with per-class adaptation gave 482 (bursts), and neither is the asynchronous irregular state a living network shows.
Optic flow
The pattern of apparent motion in an image as the viewer or scene moves; computed here with the Farnebäck algorithm16 and split into per-eye translation and expansion.
WLED · DDP
Open-source ESP32 LED firmware and the UDP protocol it accepts for real-time pixel streaming.
Strandbeest
Theo Jansen's wind-walking linkage. Ours is driven by two continuous-rotation servos, one per side, so it steers like a tank.

References

Numbered in order of first citation; the arrow after each entry returns to that citation.

  1. Berg S, Beckett IR, Costa M, Schlegel P, Januszewski M, Marin EC et al. (2025). Sexual dimorphism in the complete connectome of the Drosophila male central nervous system. bioRxiv 2025.10.09.680999. doi:10.1101/2025.10.09.680999 ↑
  2. Shiu PK, Sterne GR, Spiller N, Franconville R, Sandoval A, Zhou J et al. (2024). A Drosophila computational brain model reveals sensorimotor processing. Nature 634, 210-219. doi:10.1038/s41586-024-07763-9 ↑
  3. Yorozu S, Wong A, Fischer BJ, Dankert H, Kernan MJ, Kamikouchi A et al. (2009). Distinct sensory representations of wind and near-field sound in the Drosophila brain. Nature 458, 201-205. doi:10.1038/nature07843 ↑
  4. Mamiya A, Gurung P, Tuthill JC (2018). Neural coding of leg proprioception in Drosophila. Neuron 100, 636-650.e6. doi:10.1016/j.neuron.2018.09.009 ↑
  5. Dickinson MH (1999). Haltere-mediated equilibrium reflexes of the fruit fly, Drosophila melanogaster. Philosophical Transactions of the Royal Society of London B 354, 903-916. doi:10.1098/rstb.1999.0442 ↑
  6. Hall JM, McLoughlin DP, Kathman ND, Yarger AM, Mureli S, Fox JL (2015). Kinematic diversity suggests expanded roles for fly halteres. Biology Letters 11, 20150845. doi:10.1098/rsbl.2015.0845 ↑
  7. Maisak MS, Haag J, Ammer G, Serbe E, Meier M, Leonhardt A et al. (2013). A directional tuning map of Drosophila elementary motion detectors. Nature 500, 212-216. doi:10.1038/nature12320 ↑
  8. Wu M, Nern A, Williamson WR, Morimoto MM, Reiser MB, Card GM et al. (2016). Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs. eLife 5, e21022. doi:10.7554/eLife.21022 ↑
  9. Ache JM, Polsky J, Alghailani S, Parekh R, Breads P, Peek MY et al. (2019). Neural basis for looming size and velocity encoding in the Drosophila giant fiber escape pathway. Current Biology 29, 1073-1081.e4. doi:10.1016/j.cub.2019.01.079 ↑
  10. Klapoetke NC, Nern A, Peek MY, Rogers EM, Breads P, Rubin GM et al. (2017). Ultra-selective looming detection from radial motion opponency. Nature 551, 237-241. doi:10.1038/nature24626 ↑
  11. Nern A, Loesche F, Takemura S-y, Burnett LE, Dreher M, Gruntman E et al. (2025). Connectome-driven neural inventory of a complete visual system. Nature 641, 1225-1237. doi:10.1038/s41586-025-08746-0 ↑
  12. Schretter CE, Hindmarsh Sten T, Klapoetke N, Shao M, Nern A, Dreher M et al. (2025). Social state alters vision using three circuit mechanisms in Drosophila. Nature 637, 646-653. doi:10.1038/s41586-024-08255-6 ↑
  13. Cowley BR, Calhoun AJ, Rangarajan N, Ireland E, Turner MH, Pillow JW et al. (2024). Mapping model units to visual neurons reveals population code for social behaviour. Nature 629, 1100-1108. doi:10.1038/s41586-024-07451-8 ↑
  14. Bidaye SS, Laturney M, Chang AK, Liu Y, Bockemühl T, Büschges A et al. (2020). Two brain pathways initiate distinct forward walking programs in Drosophila. Neuron 108, 469-485.e8. doi:10.1016/j.neuron.2020.07.032 ↑
  15. Klapoetke NC, Nern A, Rogers EM, Rubin GM, Reiser MB, Card GM (2022). A functionally ordered visual feature map in the Drosophila brain. Neuron 110, 1700-1711.e6. doi:10.1016/j.neuron.2022.02.013 ↑
  16. Farnebäck G (2003). Two-frame motion estimation based on polynomial expansion. Image Analysis (SCIA 2003), Lecture Notes in Computer Science 2749, 363-370. doi:10.1007/3-540-45103-X_50 ↑
  17. Lappalainen JK, Tschopp FD, Prakhya S, McGill M, Nern A, Shinomiya K et al. (2024). Connectome-constrained networks predict neural activity across the fly visual system. Nature 634, 1132-1140. doi:10.1038/s41586-024-07939-3 ↑
  18. Eckstein N, Bates AS, Champion A, Du M, Yin Y, Schlegel P et al. (2024). Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 187, 2574-2594.e23. doi:10.1016/j.cell.2024.03.016 ↑
  19. Shiu PK, Sterne GR, Engert S, Dickson BJ, Scott K (2022). Taste quality and hunger interactions in a feeding sensorimotor circuit. eLife 11, e79887. doi:10.7554/eLife.79887 ↑
  20. Tastekin I, de Haan Vicente I, Beresford RJ, Morris BJ, Beckett I, Schlegel P et al. (2025). From sensory detection to motor action: the comprehensive Drosophila taste-feeding connectome. bioRxiv 2025.08.25.671814. doi:10.1101/2025.08.25.671814 ↑
  21. Franconville R, Beron C, Jayaraman V (2018). Building a functional connectome of the Drosophila central complex. eLife 7, e37017. doi:10.7554/eLife.37017 ↑
  22. Hulse BK, Haberkern H, Franconville R, Turner-Evans DB, Takemura S-y, Wolff T et al. (2021). A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection. eLife 10, e66039. doi:10.7554/eLife.66039 ↑
  23. Chou YH, Spletter ML, Yaksi E, Leong JCS, Wilson RI, Luo L (2010). Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe. Nature Neuroscience 13, 439-449. doi:10.1038/nn.2489 ↑
  24. Seki Y, Rybak J, Wicher D, Sachse S, Hansson BS (2010). Physiological and morphological characterization of local interneurons in the Drosophila antennal lobe. Journal of Neurophysiology 104, 1007-1019. doi:10.1152/jn.00249.2010 ↑
  25. Liu WW, Wilson RI (2013). Glutamate is an inhibitory neurotransmitter in the Drosophila olfactory system. Proceedings of the National Academy of Sciences of the USA 110, 10294-10299. doi:10.1073/pnas.1220560110 ↑
  26. Turner GC, Bazhenov M, Laurent G (2008). Olfactory representations by Drosophila mushroom body neurons. Journal of Neurophysiology 99, 734-746. doi:10.1152/jn.01283.2007 ↑
  27. Teeter C, Iyer R, Menon V, Gouwens N, Feng D, Berg J et al. (2018). Generalized leaky integrate-and-fire models classify multiple neuron types. Nature Communications 9, 709. doi:10.1038/s41467-017-02717-4 ↑
  28. Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA et al. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456-492. doi:10.1016/j.cell.2015.09.029 ↑
  29. Brette R, Gerstner W (2005). Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of Neurophysiology 94, 3637-3642. doi:10.1152/jn.00686.2005 ↑
  30. Schlegel P, Yin Y, Bates AS, Dorkenwald S, Eichler K, Brooks P et al. (2024). Whole-brain annotation and multi-connectome cell typing of Drosophila. Nature 634, 139-152. doi:10.1038/s41586-024-07686-5 ↑
  31. Nagel KI, Wilson RI (2011). Biophysical mechanisms underlying olfactory receptor neuron dynamics. Nature Neuroscience 14, 208-216. doi:10.1038/nn.2725 ↑
  32. Izhikevich EM (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks 14, 1569-1572. doi:10.1109/TNN.2003.820440 ↑
  33. Potjans TC, Diesmann M (2014). The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral Cortex 24, 785-806. doi:10.1093/cercor/bhs358 ↑
  34. Azevedo AW, Dickinson ES, Gurung P, Venkatasubramanian L, Mann RS, Tuthill JC (2020). A size principle for recruitment of Drosophila leg motor neurons. eLife 9, e56754. doi:10.7554/eLife.56754 ↑
  35. Kazama H, Wilson RI (2008). Homeostatic matching and nonlinear amplification at identified central synapses. Neuron 58, 401-413. doi:10.1016/j.neuron.2008.02.030 ↑
  36. Sen R, Wu M, Branson K, Robie A, Rubin GM, Dickson BJ (2017). Moonwalker descending neurons mediate visually evoked retreat in Drosophila. Current Biology 27, 766-771. doi:10.1016/j.cub.2017.02.008 ↑
  37. Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu S-c, McKellar CE et al. (2024). Neuronal wiring diagram of an adult brain. Nature 634, 124-138. doi:10.1038/s41586-024-07558-y ↑
  38. Hampel S, Franconville R, Simpson JH, Seeds AM (2015). A neural command circuit for grooming movement control. eLife 4, e08758. doi:10.7554/eLife.08758 ↑
  39. Sapkal N, Mancini N, Kumar DS, Spiller N, Murakami K, Vitelli G et al. (2024). Neural circuit mechanisms underlying context-specific halting in Drosophila. Nature 634, 191-200. doi:10.1038/s41586-024-07854-7 ↑
  40. Turner-Evans D, Wegener S, Rouault H, Franconville R, Wolff T, Seelig JD et al. (2017). Angular velocity integration in a fly heading circuit. eLife 6, e23496. doi:10.7554/eLife.23496 ↑
  41. Wang-Chen S, Stimpfling VA, Lam TKC, Özdil PG, Genoud L, Hurtak F et al. (2024). NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila. Nature Methods 21, 2353-2362. doi:10.1038/s41592-024-02497-y ↑
  42. Jin Z, Zhu Y, Zhang C, Sui Y (2026). Whole-brain connectomic graph model enables whole-body locomotion control in fruit fly. arXiv 2602.17997. doi:10.48550/arXiv.2602.17997 ↑
  43. Zanichelli N, Schons M, Freeman I, Shiu P, Arkhipov A (2025). State of Brain Emulation Report 2025. arXiv 2510.15745. doi:10.48550/arXiv.2510.15745 ↑
  44. Wang F, Theilman BH, Rothganger F, Severa W, Vineyard CM, Aimone JB (2025). Neuromorphic simulation of Drosophila melanogaster brain connectome on Loihi 2. arXiv 2508.16792. doi:10.48550/arXiv.2508.16792 ↑
  45. Wilson RI (2013). Early olfactory processing in Drosophila: mechanisms and principles. Annual Review of Neuroscience 36, 217-241. doi:10.1146/annurev-neuro-062111-150533 ↑
  46. Olsen SR, Wilson RI (2008). Lateral presynaptic inhibition mediates gain control in an olfactory circuit. Nature 452, 956-960. doi:10.1038/nature06864 ↑
  47. Lin AC, Bygrave AM, de Calignon A, Lee T, Miesenböck G (2014). Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination. Nature Neuroscience 17, 559-568. doi:10.1038/nn.3660 ↑
  48. Nanami T, Yamada D, Someya M, Hige T, Kazama H, Kohno T (2024). A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support. Frontiers in Neuroscience 18, 1384336. doi:10.3389/fnins.2024.1384336 ↑
  49. Brunel N (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience 8, 183-208. doi:10.1023/A:1008925309027 ↑
  50. Li Q, Ping W, Zhang K, Wang C (2026). Connectome-constrained modeling identifies neurons and synapses that sustain spontaneous activity in Drosophila. bioRxiv 2026.08.21.745055. doi:10.64898/2026.08.21.745055 ↑
  51. Aso Y, Hattori D, Yu Y, Johnston RM, Iyer NA, Ngo T-TB et al. (2014). The neuronal architecture of the mushroom body provides a logic for associative learning. eLife 3, e04577. doi:10.7554/eLife.04577 ↑
  52. Hige T, Aso Y, Modi MN, Rubin GM, Turner GC (2015). Heterosynaptic plasticity underlies aversive olfactory learning in Drosophila. Neuron 88, 985-998. doi:10.1016/j.neuron.2015.11.003 ↑
  53. Handler A, Graham TGW, Cohn R, Morantte I, Siliciano AF, Zeng J et al. (2019). Distinct dopamine receptor pathways underlie the temporal sensitivity of associative learning. Cell 178, 60-75. doi:10.1016/j.cell.2019.05.040 ↑
  54. Takemura S-y, Aso Y, Hige T, Wong A, Lu Z, Xu CS et al. (2017). A connectome of a learning and memory center in the adult Drosophila brain. eLife 6, e26975. doi:10.7554/eLife.26975 ↑
  55. Kakaria KS, de Bivort BL (2017). Ring attractor dynamics emerge from a spiking model of the entire protocerebral bridge. Frontiers in Behavioral Neuroscience 11, 8. doi:10.3389/fnbeh.2017.00008 ↑
  56. Turner MH, Krieger A, Pang MM, Clandinin TR (2022). Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila. eLife 11, e82587. doi:10.7554/eLife.82587 ↑
  57. Kamikouchi A, Inagaki HK, Effertz T, Hendrich O, Fiala A, Göpfert MC et al. (2009). The neural basis of Drosophila gravity-sensing and hearing. Nature 458, 165-171. doi:10.1038/nature07810 ↑

Software and data

  1. HHMI Janelia FlyEM Project Team, Google Research MaleCNS v1.0: the male Drosophila central nervous system connectome, data release. Website. https://www.janelia.org/project-team/flyem/male-cns-connectome ↑
  2. nftechie DOOMFLY: the MaleCNS connectome as a spiking model driving Doom (MIT licence). GitHub repository. https://github.com/nftechie/doomfly ↑
  3. ornata Fly64: MaleCNS connectome simulation with background drive and noise. GitHub repository. https://github.com/ornata/fly ↑
  4. 3waylabs DDP: Distributed Display Protocol. Specification. http://www.3waylabs.com/ddp/ ↑
  5. WLED project WLED: open-source LED firmware for ESP32. Documentation. https://kno.wled.ge/ ↑
  6. Virtual Fly Brain Virtual Fly Brain: cell-type ontology, curated transmitters and cross-connectome connectivity for Drosophila (Court R, Costa M, Pilgrim C et al. 2023, Frontiers in Physiology 14, doi:10.3389/fphys.2023.1076533). Database. https://virtualflybrain.org ↑
  7. OpenCV Optical flow (calcOpticalFlowFarneback), OpenCV 4.x tutorial. Documentation. https://docs.opencv.org/4.x/d4/dee/tutorial_optical_flow.html ↑
  8. flybrain.online flybrain.online: browser demo of the MaleCNS connectome. Website. https://flybrain.online/ ↑
  9. eonsystems PBC fly-brain: GeNN, Brian2CUDA and PyTorch benchmarks of the fly connectome (GPL-2). GitHub repository. https://github.com/eonsystemspbc/fly-brain ↑
  10. DenisSergeevitch desktop-fly: MaleCNS connectome with looming from the cursor and leg animation from motor neurons. GitHub repository. https://github.com/DenisSergeevitch/desktop-fly ↑
  11. Shiu PK Drosophila_brain_model: Brian2 code for the computational brain model of Shiu et al. 2024. GitHub repository. https://github.com/philshiu/Drosophila_brain_model ↑
  12. mrfly.dev mrfly: MaleCNS connectome demo. Website. https://mrfly.dev/ ↑
  13. snedea flybrain: FlyWire connectome as LIF neurons in a browser worker. GitHub repository. https://github.com/snedea/flybrain ↑
  14. lixiang1076 fly-brain. GitHub repository. https://github.com/lixiang1076/fly-brain ↑
  15. evnsnclr NeuroCraft Fly: fly connectome in Minecraft (no code released). GitHub repository. https://github.com/evnsnclr/neurocraft-fly-public ↑
  16. IONOFIELD FLYCNS: benchmarked spiking simulation of the Drosophila central nervous system (MaleCNS v1.0, the Shiu et al. model ported and held to escape, auditory and feeding criteria). GitHub repository. https://github.com/IONOFIELD/FLYCNS ↑
  17. annel0 flybrain: a GPU simulator for connectome-constrained spiking models of the Drosophila CNS (MaleCNS v1.0 and FlyWire), with a record of what it could not establish. GitHub repository. https://github.com/annel0/flybrain ↑
  18. gianlucamazza flymsg: a validated whole-CNS spiking model of MaleCNS v1.0 after Shiu et al. 2024, with the weight scaled for synapse density. GitHub repository. https://github.com/gianlucamazza/flymsg ↑

The fruit fly in the section breaks is a side-view silhouette of Drosophila melanogaster by Maarten van der Sande, from PhyloPic, dedicated to the public domain (CC0 1.0); here resized and recoloured.

Changelog

  • 12/09: Page created: the loop, what the fly sees, inside the connectome, one neuron, light and legs, lineage, numbers, glossary; layouts made overlap-free; introduction written for a first-time reader.
  • 13/09: Add the body-map section (every LED traced to its motor neurons, generated from the map); correct the haltere feedback direction and the IMU mount rotation.
  • 14/09: Add this change log; embed the rig video beside the introduction; record the deviations made since 12/09: per-class spike-frequency adaptation (new for a whole-CNS model), the lobula-plate and small-object visual channels, the hybrid steering readout and its reverse rule, the robust loom detector, real-time pacing, and the resolution of the DNa02 asymmetry.
  • 15/09: Figure 2 shows the lobula-plate (LLPC1/LPC1) and small-object (LC10c/d) channels and the small-target detector; video re-encoded and stretched to the intro; authorship note; live runs back to no adaptation.
  • 16/09: Numbered citations throughout and a References section generated from a bibliography file; every source the page rests on is listed with its DOI; project file paths removed from the page. 16/09: Section 8, seven open questions on the model and the robot, each with its literature, its expected outcomes and its ticket.; internal ticket references removed; page widened, open questions in two columns and references in three; figures numbered and referenced by number; the colour key of Figure 3 moved inside the figure; Figure 2 shows the LC9 channel and the current detector, its arrow rerouted
  • 18/09: Corrections after a review against Virtual Fly Brain and the literature, now a cited source: LC11 tuned to objects of about 4.5 to 5 degrees; LC9 as the documented DNp09 input, LC10c/d reaching the right DNp09 only and their family a courtship-tracking channel; the latch loop's excitatory cells and their disputed signs; transmitter accuracy quoted from the MaleCNS paper; LLPC1/LPC1 as a direct input to DNa02 beside its central-complex inputs; adaptation values marked as inferred where not measured; DNa01 as a turning neuron; BPN's MaleCNS types named; the gyro drives nothing, since a fruit fly's halteres are flight sensors held still while walking, while tilt goes to Johnston's organ and wheel speed to the leg chordotonals. 18/09: The live model stated once: 0.31 mV per contact, calibrated on this connectome by Shiu's own MN9 criterion, and the lLN1_bc local neurons signed GABAergic as curated, both recorded as deviations at Figure 4 and in the lineage table; the taste neurons typed by modality with MN9 as the readout and the gate numbers under the live model; the two latch loops named with their cells at the ceiling; Figure 2 splits the loom into angular velocity for LC4 and angular size for LPLC2, with the Giant Fiber result; the LC9 route under the live weights; the haltere populations measured as a wing pathway; open questions 1 and 2 rewritten around the resting state and the rest of Shiu's validation; three sources added.
  • 19/09: The background-activity experiment recorded: spontaneous synaptic input per cell class at the rates the literature reports gives silence or the attractor and nothing between, so it stays off (open question 1, the lineage table, the unit); adaptation under it (open question 5); the term's cost in the numbers; glossary: Fano factor. 19/09: Section 8, Findings: ten results of the project's own testing not found in the sources consulted (the antennal lobe's resting gain, background input giving silence or the attractor, the two attractors and which is structural, the weight scale on this connectome, the transmitter call behind the olfactory latch, the high-salt drive of MN9, the pursuit neuron's onset-only response, the haltere afferents as a wing pathway, LC10's one-sided route to DNp09, MN9's asymmetry), each with its measurement, its bounds, the nearest literature and a reproduction line; the open questions renumbered 9; seven sources added.
  • 21/09: The live model gains two recorded deviations, the class-majority sign for the antennal lobe's 67 local neurons of undecided transmitter and the Kenyon cells' measured threshold gap, stated at Figure 4 and in the lineage and numbers tables; the antennal-lobe / mushroom-body loop gone and the feeding gate re-measured under them; Shiu's validation carried to this connectome (124 of 164); the robot's camera image rotated and mirrored so each eye sees its own side, frames at 3 to 5 per second on the floor.
  • 23/09: GLNO signed inhibitory as measured, the fifth deviation; Figure 3 draws the ring loop as the four self-sustaining modules that remain, with the olfactory circuit at rest; the gate at 96 % of Shiu's criterion; Findings 1 to 7 and 10 brought up to date and three added (the dopaminergic cells as fast excitation in the mushroom body, Shiu's validation on a second connectome, the reciprocal pairs that hold the ring loop); open questions 1, 2, 5 and 6 rewritten around the ring loop, the silencing shortfall, adaptation's trade-off and the first floor run that oscillates about a heading; Virtual Fly Brain's disagreement on two transmitters recorded; glossary: Kenyon cell, ring loop; eleven sources added; the introduction names the robot's own camera as its eye; on a phone the tables scroll inside themselves instead of widening the page, and each glossary term sits above its definition; figure captions shortened, their explanations moved into the text; Shiu's validation re-run under the live model, 120 of 164, with the ten rows that moved and the all-or-none water switch in finding 12 and question 2; a simplified reading, a short plain-language note beside each paragraph group of sections 1 to 9, every finding and every open question, shown in place of the text by a Simple switch where the screen is too narrow for both (remembered between visits); the contents in a column on the left that marks the section being read, on a phone a menu that slides in from the left below a bar fixed to the top of the screen, which names the section being read and holds the Detailed / Simple switch; the open questions in one column; a new look: a muted paper-and-ink palette with an ochre accent, Scorekard for titles and Cartridge for labels, section numbers set apart in the accent instead of a middle dot, smaller citation numbers, and Figures 3 and 4 recoloured to the palette while every drawing of the LED panel keeps the panel's own colours; the three data tables are drawn as tables, on a white ground with cell borders and a short numbered caption; the simplified notes are set in clay; a wider gap between the text and its notes; generous space above and below figures and tables; a clay rule beside each note; more room inside the boxes of Figure 2; a fruit-fly section break after the introduction and before Numbers, marking the main content; Changes renamed Changelog; a new first section, the setup, gives the rig in three paragraphs and one simple drawing, so the later sections and figures move up by one; the Numbers table removed; Figure 2's camera box names both cameras, the desk webcam and the robot's own; the introduction rewritten, with a line on where the simplified reading is found; table headings on the same white ground as the table.
  • 27/09: Finding 14 added, adaptation by cell class as the resting-state candidate, from the benchmark with it off and on: no latched module or ceiling cell after the test pulses, the compass neurons in their measured band, the eyes' motion responses mirrored, at the cost of a third of the feeding response, a lower escape response and a bursting population; open questions 1, 5 and 6 and findings 7, 12 and 13 brought up to date with it; Shiu's validation given as the benchmark now scores it, 130 of 164, a change of scoring beside 120 under his own criterion.