Brain and body
A 57,000-point nervous system shaped after the Drosophila neuropils, a fly-eye view through 750 ommatidia, tarsal adhesion, gaze contrast and viewing distance, all live.
Embodied Drosophila literacy simulation
A fruit fly learning to read, and the flies who teach and proctor it
Fruit Fly Lingo puts a simulated fruit fly in front of the screens we use to learn: a phone that teaches word parts, a laptop that asks it to spell, a gradebook, a proctoring grid, a classroom of 24 flies with tablets in mating season, a phonics app that asks it to say the word, which it sings back with one wing, a tablet of illustrated stories it reads aloud while hovering, a writing app where it answers in cursive with the tip of its abdomen, a VR headset that seats it at a desk in a third grade class taught by another fly, a glass AR headset that fills the table with holographic word blocks, a Scrabble board where it plays a butterfly, a hardback picture book whose pages it turns with its own wings, and a globe on a school desk for geography trivia. A stylised fly brain lights up with what it does, a dopamine reward strengthens its memory of each prefix, root and suffix, and psychometric dashboards measure the learning as it happens. It is the interaction layer of the Embodied Drosophila Literacy Simulation, a study of whether a nervous system evolved for finding food can be repurposed for symbolic learning.
Fifteen experiments
Thirteen student activities and two staff roles. Pick one to open it in the simulator; the same fly, brain and curriculum run through all fifteen.
Experiment 1
Walking
The fly walks across a phone lying on the table, reads the meaning with head sweeps, and presses word-part tiles with a foreleg.
Student
Experiment 2
Flying
It climbs to read the meaning, then dives and lands on its answer. Flight paths and a landing heatmap build up over trials.
Student
Experiment 3
Touch
A phone at the fly's own scale, held at the bottom edge. It swipes the answers into reach and taps one.
Student
Experiment 4
Spelling
A real-size laptop shows the meaning and the word-part hints. The fly flies key to key and presses each letter to spell the word.
Student
Experiment 5
Teacher
A teacher fly grades the four students from a fly-sized laptop, steering the cursor with one foreleg on the trackpad. The screen is never touched.
Staff
Experiment 6
District admin
Nine webcam sessions on one screen, a vision overlay on every fly, and FlyAI flagging suspected cheating for the admin to check in on.
Staff
Experiment 7
Classroom
Twenty-four students at six round tables, one tablet each, in mating season. Courtship, rejection and rivalry compete with the lesson, and everyone still has to finish.
Student
Experiment 8
Phonics
The app says a word and the fly says it back, sound by sound, by singing with one wing: pulse song for consonants, a hum for vowels. The recogniser scores every sound.
Student
Experiment 9
Reading
A tablet on a stand runs illustrated six-page stories. The fly hovers and reads aloud in its buzz, taps Next, answers Stop & Think questions, and picks its next story.
Student
Experiment 10
Writing
Greek root questions answered in joined cursive. The fly flies nose-up and drags the tip of its abdomen across the glass, then taps Submit to have its handwriting read.
Student
Experiment 11
VR
A fly-sized visor, cabled to a ball treadmill and a laptop. Inside, the student sits at a desk in a third grade class and answers in pencil, while a teacher fly on a second rig chalks word parts on the board.
Student
Experiment 12
AR
A glass headset puts holographic word blocks in the room. The fly walks around them, pinches one with a foreleg, carries it to the word frame, rotates it and snaps it in.
Student
Experiment 13
Scrabble
Fruit fly against butterfly on a full board. The fly walks each tile from its rack to its square; the butterfly flies its tiles in. Each learns the words the other plays.
Student
Experiment 14
Paper book
Flynn and the Giant Peach as a hardback picture book. The fly reads it aloud word by word, then grips the page corner and lifts the page over with its wings.
Student
Experiment 15
Geography
Trivia on a globe with real country outlines. The fly reads the card, flies to the globe, spins it round and taps the country; it lights green if right, red if wrong.
StudentWhat you can measure
Learning
The dopamine rule Δw = η(R − V) on every answer, G-DINA attribute mastery, Half-Life Regression forgetting curves, and a Rasch Wright map with standard errors.
Navigation and proctoring
Fitts' law for the teacher's cursor, grading error against real accuracy, and proctoring flags scored against seeded cheating with precision and response time.
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Looking at the screen
Tarsal pads gripping glass
Physics 800 Hz · 102 DoF
Experiment 1 · Walking. The fly walks across a phone lying on the table and presses tiles with a foreleg.
Loading the arena…
EDLS dashboard suites
Learning and metrics, live from the simulation
Four dashboards from the psychometric specification, fed by the running fly. Values update while the simulation runs in the Simulate view. Where a quantity is a stand-in for a sensor or model the app does not yet have, the card says so.
Current trial
Experiment 1 · WalkingBuild the word that means
able to be counted again
Word parts placed0 of 3
The fly is looking for the prefix.
Session
0answers
0%correct
0words built
Answers per minute0Mean landing error0 pxDifficulty level1 of 6Curriculum resets0
Dashboard 1 · Kinematic and sensorimotor performance
Tarsal adhesion
µN, pads in contact00 of 6 pads on the glass
Stand-in: about 12 µN per tarsal pad in contact, the order of measured Drosophila pad forces. The MuJoCo adhesion actuators will replace it.
Ommatidial contrast
ΔL under the gaze0.00luminance spread in a 40 px patch where the eye axis meets the glass
Sampled from the real screen texture at the point the head camera looks at.
Joint torque proxy
59 channel norm0summed foot speed across six legs, scaled
Stand-in for the 59-dimensional action vector until the physics body exists.
Microsaccadic sampling
where the eye axis meets the screenHead sweeps during look and hover phases trace the path. Sequential contrast changes along it are what resolve letters at a 4.5° inter-ommatidial angle.
Acuity
eye to glass, 1 to 10 mm band0.0 mmnow
0%of time in band
Dashboard 2 · Connectomic and neurophysiological monitoring
Synaptic message passing
afferent → intrinsic → efferentAcetylcholine, glutamate: excitatoryGABA, histamine: inhibitory
PAM reinforcement
Δw = η (R − V), η = 0.22Last reward R–Expected value V (memory before)–Δw applied–
Each bar is one answer. Green raised the Kenyon cell to MBON weight of the needed word part; red lowered it.
Differential memory decay
STM versus LTM compartmentsγ lobe STM, decays in about 25 sα/β lobe LTM, consolidated weight
Neuro-connectivity matrix
Region-level stand-in: fibre tracts of the stylised brain, scaled by live co-activity. Rows for the ventral nerve cord are absent from FAFB, which stops at the brain.
Regions each activity drives
mean activity by experiment, scaled to the busiest cellEach row is an experiment, each column a group of neuropils. The deeper the amber, the more that activity drives the region: the optic lobes while reading, the leg neuropils while walking or writing, the mushroom bodies when a reward lands.
Motivation
dopamine against each reward and errordopamineright answerwrong answer
A right answer lifts dopamine and a wrong one drops it; between answers it drifts back to 0.5. The PAM rule above uses the same signal to change a memory.
Dashboard 3 · Psychometric and cognitive diagnosis
Q-matrix
tasks to latent attributes| Task | α1 Contrast edge detection | α2 Shape discrimination | α3 Sequential symbol tracking | α4 Precision motor targeting |
|---|---|---|---|---|
| Symbol identification | Yes | Yes | No | Yes |
| Phonetic sequence | Yes | Yes | Yes | Yes |
| High-contrast touch | Yes | No | No | Yes |
G-DINA attribute mastery
posterior, mastered at 0.80Proxies from the running session: α1 from contrast under the gaze while reading, α2 from accuracy when distractors share the needed kind, α3 from root and suffix placements, α4 from landing error.
Half-Life Regression
p(recall) = 2^(−Δt / h)One simulated minute counts as one day. Half-life grows with the weight: h = 0.5 · 2^(4m) days.
Wright map
Many-Facet Rasch, one logit scaleθ from the logit of overall accuracy with its standard error; β per task from the logit of that task's failure rate. S-X² is approximated from observed against expected success per difficulty level and flagged at 1.5 or above.
Memory for each word part
every part the fly has met, over the sessionstrongest partsweakest partthe rest
Every part starts at 0.2. A right answer moves its memory 22% of the way to 1; a wrong one takes a little away. Sampled every two seconds.
Dashboard 4 · Curriculum adaptation and sequence policy
xLSTM memory analytics
mLSTM state, 8 featuresC_t = f_t C_{t−1} + i_t v_t k_tᵀ with the stabiliser m_t and normaliser n_t, run on keys from the fly's sensory stream and values from the task state. A toy cell, not the trained model; TFLA kernels apply once inference moves to WebGPU.
Decision Transformer
return-to-go and policy divergenceReturn-to-go target (dopamine)0.58Achieved (dopamine now)0.50Difficulty level1 of 6Correct streak0
Divergence is the mean distance between the fly's actual path and the ghosted optimal path to the correct tile (shown dashed in the arena with paths on). Above 10 the curriculum resets a level.
Dashboard 5 · Teacher navigation
Experiment 5. A teacher fly grades the flies from Experiments 1 to 4 in a gradebook on a fly-sized laptop, using only the trackpad and keys. Their rows are this session's real trials, so the other experiments are what the teacher is reading.
Grading session
not running0students graded
0clicks
0misclicks
Keystrokes0Cursor path0 pxDistance flown0 mmDistance walked0 mmMean time per student–Mean grading error–
Grading error is the gap between the grade the teacher typed and the student's real accuracy. The teacher misreads digits less as it practises.
Fitts' law
ID = log₂(D/W + 1) against movement timeFitted MT = a + b·ID–Fit R²–Hits / attempts0 / 0
Each dot is one cursor movement to a target: D the distance, W the target's smaller side. Green landed on the target, red missed. The amber line is the least-squares fit over the moves shown.
Cursor heatmap
where the teacher clickedDrawn over a miniature of the gradebook. Rows and buttons the fly aims for should light up; scatter elsewhere is misclicks.
Class roster
the students, live| Student | Answers | Correct | Words | Grade given | Error |
|---|
Time by activity
reading · steering the cursor · typing · flyingDashboard 6 · District proctoring
Experiment 6. A district admin fly watches nine students on a 3 × 3 grid of webcam sessions, with a computer-vision overlay and FlyAI recommendations. Three of the nine are seeded with a cheating behaviour; the dashboard scores the flags against that hidden truth.
Proctoring session
not running0FlyAI flags
0confirmed
0false alarms
Check-ins sent0Precision of flags–Cheaters caught0 of 3Mean response time–Admin clicks / misclicks0 / 0
A flag is confirmed when the student it names was seeded to cheat. Response time runs from the FlyAI flag to the admin's check-in click.
Attention by student
eye contact with the task, smoothedFrom the vision overlay: share of recent frames where the gaze line meets the student's own screen. Red names are seeded cheaters; the admin does not see this column.
Detections
what the overlay sees, by reasonlooks awaysecond devicehelper flyoff camera
Share of webcam frames showing each behaviour, over the session. The line under the chart says how many flags turned out to be real.
FlyAI log
recommendations and admin actionsDashboard 7 · Classroom social dynamics
Experiment 7. Twenty-four flies share six round tables in mating season. Everyone does the same twelve word-part items; what differs is how much of each fly's time the room takes. Courtship, rejection and rivalry follow the Drosophila literature, and a rejected male courts less afterwards, so the class settles as a lesson goes on.
Class session
not running0finished of 24
0%on task now
0courting now
Lesson · elapsed–Courtship attempts0Rejections: ignored · flicked · decamped0Courtships tolerated0Rival fights0Glances at a neighbour's tablet0Grooming bouts0Class accuracy–
A glance at a tablemate who is further ahead raises the next answer's chance of being right: social copying. Being sung to lowers it.
Time budget by sex
share of each group's time this sessionon taskcourtinggrooming, glancingaway
Males lose time courting. Females lose it to being courted: rejecting, decamping, and the attention cost of a male singing beside them.
The room over time
24 flies: finished · on task · courtingfinishedon taskcourtingclass mastery, dashedmale drive, dotted
Each rejection cuts a male's courtship drive by about a third, the courtship-conditioning effect; it recovers a little between lessons.
Courtship network
attempts, male row to female columnWho courted whom. Tablemates dominate, because reaching another table takes a flight.
Lessons
seconds until all 24 finishedin seasonout of season
Turn mating season off in the simulator to compare. A lesson ends only when its last fly finishes.
Roster
all 24, live| Fly | Doing | Items | Accuracy | On task | Courted · courting | Rejections given · received | Drive · receptivity | Attention |
|---|
Dashboard 8 · Phonics and wing song
Experiment 8. The app says a word and the fly sings it back sound by sound with one wing. The recogniser scores each sound against the word; a letter-sound memory per phoneme learns with the same Δw = η(R − V) rule as the word parts.
Phonics session
not running0words said right
0sounds sung
–sounds right
Attempts · retries0 · 0Current word–Mean letter-sound memory–Carrier pitch–Soundoff
A word gets two attempts. The chance of singing a sound right is 0.3 + 0.65 × its memory, so sounds the fly has practised come out right more often.
Letter-sound memory
per phoneme, with accuracyGrouped by how the fly sings them: pulse song for stops, a hum for vowels and nasals, a hiss for fricatives.
Confusions
target sound row, what was heard columnheard rightmixed up
Each row is shared out by what was heard, so a full blue diagonal means every sound is heard right. Off-diagonal cells cluster within a kind of sound, the errors a phonics teacher expects: /k/ for /t/, /s/ for /ʃ/.
Last attempt
spectrogram of the buzz, 0 to 8 kHzHow a fly sings a word
sound kind to song mode| Sounds | Song mode | In real flies |
|---|---|---|
| Stops · /b d g k p t/ | Pulse song, three pulses 35 ms apart, filtered at the consonant's burst frequency | Pulse song, interpulse interval about 35 ms |
| Vowels · /æ ɛ ɪ ɒ ʌ/ | A sawtooth hum at 165 to 205 Hz shaped by the vowel's first two formants | Sine song, a hum near 150 Hz |
| Fricatives · /f s ʃ tʃ h/ | Band-passed hiss fluttered at the wingbeat | Not in the song; the wing's noise |
| Nasals and liquids · /m n l r/ | A low muffled hum, or a gliding one | Sine song variants |
Dashboard 9 · Reading stories aloud
Experiment 9. The fly reads six-page illustrated stories from a tablet on a stand, aloud in its wing buzz, taps Next to turn pages, answers a Stop & Think question after page 3 and a comprehension question after page 6, then chooses the next story from the library.
Reading session
not running0stories finished
0words read aloud
–words/min, last page
Now reading–Pages read0Miscues · self-corrected0 · 0Accuracy–Stop & Think and comprehension0 of 0Mean vocabulary memory–
A miscue is a word read wrong; most are self-corrected. Unfamiliar vocabulary words are read more slowly and misread more often until their memory grows.
Vocabulary memory
bold words, by storyEach time the fly reads a bold word correctly its memory grows by Δw = η(R − V); a misread lowers it.
Fluency by page
words per minute, coloured by accuracy95% or betterbelow 95%5-page mean
Dashed lines mark a new story. Timed in simulation seconds, so the rate does not change with the speed buttons.
The library
four stories, six pages each| Story | Theme | Grade | Words to know | Read |
|---|
Illustrations are AI-generated from each page's image prompt; a page without an image is painted in the browser instead.
Reading log
miscues, questions, storiesDashboard 10 · Writing Greek roots in cursive
Experiment 10. The fly answers Greek root questions by writing in cursive with the tip of its abdomen while it flies, then taps Submit and the app reads the handwriting. Right answers depend on a memory per root; legibility depends on a motor skill that grows with practice.
Writing session
not running0answers right
0answers written
–legibility, last
Letters written0Ink laid down0 pxLetters per minute, last–Motor skill · wobble–Misread by the recogniser0
Legibility falls as the wobble of the abdomen tip grows. Below about 55% the recogniser can read one letter as another, so a right answer can be marked wrong.
Greek root memory
per root, with its meaningThe chance of writing the right answer is 0.3 + 0.65 × the root's memory, which learns with Δw = η(R − V).
Legibility over answers
each answer, with a rolling meanread back rightmisread, hollow6-answer mean
The fly's tail control improves a little with every answer.
The roots
twelve Greek roots| Root | Meaning | As in | Tries | Right |
|---|
Writing log
what was written and how it was readDashboard 11 · VR classroom
Experiment 11. The fly walks on an air-supported ball in a headset that seats it at a desk in a third grade class. It watches the teacher, a second fly on its own rig, write word parts on the chalkboard and answers in pencil in its booklet. Walking and turning on the ball move its view; answers train the same word part memory as the other experiments.
VR session
not running0answers right
0answers written
–accuracy
Walked on the ball0 body lengthsBall speed now–Turns to look around0Lesson · question–
The ball floats on air, so every step turns it instead of moving the fly. Forward walking bobs the student's view; a turn swings it toward a classmate.
Where the student looks
seconds in the headsetBoard while the teacher writes, booklet while the pencil moves, classmates when the fly turns on the ball.
Word part memory
shared with the other experimentsThe chance of writing the right answer is 0.35 + 0.6 × the memory of the parts on the board, which learns with Δw = η(R − V).
The booklet
what the student wrote| On the board | Answer | Written | Right |
|---|
VR log
what happened in the classDashboard 12 · AR word blocks
Experiment 12. The fly walks on the table in a glass mixed-reality headset. Holographic blocks, each carrying a word part, float around it; it walks around them, pinches one with a foreleg, carries it to the word frame, rotates it until its label faces out and snaps it into a slot. Placements train the same word part memory as the other experiments.
AR session
not running0words built
0blocks placed
–blocks right
Walked on the table0 body lengthsWalking around blocks0 sRotated in total0°Now–
The holograms float at head height, so the blocks it is not fetching are obstacles: it steers around them on the way to a block and on the way to the frame.
Rotation per block
degrees turned to face the label outright slotsent back, hollowaccuracy over the last 8, right axis
Blocks start at a random angle, so each needs a twist of the foreleg.
Word part memory
shared with the other experimentsThe chance of fetching the right block is 0.35 + 0.6 × the memory of the part the slot needs, which learns with Δw = η(R − V).
Placements
every block snapped or sent back| Word | Slot | Block | Rotated | Walked | Right |
|---|
AR log
what the fly didDashboard 13 · Scrabble, fruit fly vs butterfly
Experiment 13. The fruit fly and a Blue Morpho play Scrabble on a full board. The fly walks each tile from its rack to its square; the butterfly flies its tiles in. Each player knows only part of the word list at first and learns the words the other plays.
Match
not running0fruit fly wins
0butterfly wins
–score now
Tiles in the bag–Best word, fly–Best word, butterfly–Mean points per word–
Words must cross one already on the board, with no side words. Letter values and premium squares follow the standard game; using all seven tiles scores 50 more.
Points per word
amber fruit fly, blue butterflyEach player plays its best-scoring word most of the time, and one of its top five otherwise.
Score race
running totals through the current gamefruit flybutterfly
Words known
each learns from the otherBoth start knowing the two- and three-letter words and about half of the rest. A word the opponent plays is learned seven times in ten.
Words played
latest first| Game | Player | Word | Where | Tiles | Points |
|---|
Game log
turns, swaps, learningDashboard 14 · Reading a paper book
Experiment 14. The fly reads Flynn and the Giant Peach as a hardback picture book. It flies from word to word above the page, buzzing each one aloud, and turns every page itself: it grips the corner with both forelegs and lifts the page with its wings until it falls over.
Paper book session
not running0pages read
0words read aloud
0pages turned
Times read through0Words per minute, last page–Miscues · self-corrected0 · 0Mean time to lift a page–
A page is heavy for a fly: it comes up slowly while the wings beat, the corner leading, and once past upright it falls over on its own.
Fluency by page
words per minuteBold vocabulary words are read more slowly until their memory grows.
Page turns
seconds from grip to releaseseconds to liftmean, dashed
The lift speed wavers with each wingbeat.
Reading log
pages, stumbles, turnsDashboard 15 · Geography on the globe
Experiment 15. Trivia questions answered on a globe with real country outlines. The fly reads the card, flies to the globe, spins it, and taps the country it thinks is right. A memory per country decides how often it is right; a wrong tap is usually a neighbour, and the distance off is measured along the Earth's surface.
Geography session
not running0countries right
0questions
–accuracy
Round1Mean distance off, wrong answers–Flights0Globe turned0°
The globe leans 23.4° on its axis. Before each answer the fly sweeps it round with a foreleg until the region it wants faces it.
Distance off
km from the right countryright, on the linewrong, drops by km away
A wrong answer is one of the four countries nearest the right one.
Country memory
per country askedThe chance of tapping the right country is 0.3 + 0.65 × its memory, which learns with Δw = η(R − V).
Answers
latest first| Question | Answer | Tapped | Off by |
|---|
Geography log
questions and taps
About
What a fly brain can tell us about learning to read
Fruit Fly Lingo is the interaction layer of the Embodied Drosophila Literacy Simulation (EDLS). It puts a simulated fruit fly in front of a smartphone that teaches word parts, and asks a simple question with a hard answer: can a nervous system that evolved for finding food and avoiding swatters be repurposed to assemble prefixes, roots and suffixes?
What the app simulates
A procedural fly with six IK legs, wings and a head-mounted compound-eye camera lives on a table with a phone. The phone runs a small morphology curriculum in the style of LibreLingo and Word Root Workshop: build the word that means able to be counted again from re-, count and -able. The fly answers by touching tiles, and the student experiments vary how it gets there: walking on the glass, flying up to read and diving to land, holding a fly-sized phone and swiping, or spelling the word key by key on a laptop. Two more experiments put flies on the other side of the desk: a teacher grading those students from a fly-sized laptop, and a district admin proctoring nine of them through webcam sessions with a vision overlay and an assistant called FlyAI. A seventh puts twenty-four student flies in one classroom during mating season, where courtship, rejection and rivalry compete with the lesson. An eighth is a phonics lesson: the fly cannot speak, so it answers by singing each sound with one wing. A ninth hands it a library of illustrated stories on a tablet, which it reads aloud in its buzz, flying from word to word. A tenth asks it to write: it answers Greek root questions in cursive with the tip of its abdomen.
A stylised central nervous system, about 55,000 points laid out after Drosophila anatomy, lights up with what the fly does: optic lobes while it reads, central complex and leg neuropils while it moves, mushroom bodies and taste centres when a sugar reward arrives, lateral horn when it flinches from a wrong answer.
What we are trying to learn
Whether biological wiring is a useful inductive bias for symbolic learning. A random network can learn to tap tiles; the interesting result is a network constrained by the fly's actual circuits doing it, because then the solution has to route through the same sensorimotor pathways a real fly uses. The "Digital Sphinx" problem, a worm connectome driving a fly body, shows how easily a high-capacity learner fakes the behaviour without the biology.
How reward learning in the mushroom body maps onto a curriculum. The PAM dopamine cluster gates plasticity between Kenyon cells and output neurons; here that is the rule Δw = η(R − V) that strengthens a word part's memory after a correct answer, and the Half-Life Regression that schedules its review.
How to measure learning in a non-human agent. The dashboards apply G-DINA cognitive diagnosis, Half-Life Regression and the Many-Facet Rasch model so that "the fly learned re-" is a claim with a posterior, a half-life and a logit, not a feeling.
The student experiments
Experiment 1 in detail
| Experiment | Phone | Reading | Answering | Records |
|---|---|---|---|---|
| 1 · Walking | Table-sized, lying flat | Head sweep from where it stands | Walks to the tile, presses with a foreleg | Walking paths, press heatmap |
| 2 · Flying | Table-sized, lying flat | Climbs to a hover point over the prompt | Dives and lands on the tile; landing is the answer | Flight paths, landing heatmap |
| 3 · Touch | Fly-sized, held at the bottom edge | Reads at scroll 0 | Swipes the list, then taps | Press heatmap in content coordinates |
The dashboards
Everything is computed from the live session; where a quantity stands in for a sensor or model the app does not yet have, its card says so. The same cards dock beside the arena in the Simulate view, which opens the dashboard for whichever experiment is running.
Technical architecture
The whole application lives in one HTML file. The diagrams group it by responsibility; arrows are data or control flow at run time. They are the same diagrams as in the repository README.
Component architecture
flowchart TB
subgraph Browser["Browser · index.html"]
direction TB
subgraph UI["HUD · HTML + CSS (UF design tokens)"]
Header["Header controls<br/>experiment · speed · camera · pause"]
TrialCard["Trial card<br/>meaning · placed chips · progress · feedback"]
StatePills["State pills<br/>phase · adhesion · physics"]
BrainHUD["Brain panel HUD<br/>dopamine · region bars · memory · stats"]
EyeLabel["Fly-eye label"]
end
subgraph Game["Curriculum and game state"]
WORDS["WORDS[]<br/>word · meaning · parts · glosses"]
Distractors["DISTRACTORS[]"]
GameState["game<br/>wordIdx · placed · tiles · flash<br/>memory Map · dopamine · taps · correct"]
buildTiles["buildTiles()<br/>remaining parts + distractors, shuffled"]
registerTap["registerTap(i)<br/>correct → memory↑ dopamine↑ placed++<br/>wrong → memory↓ dopamine↓ flinch"]
nextWord["nextWord()"]
end
subgraph Screen["Phone screen"]
ScreenCanvas["2D canvas 390×844<br/>drawScreen(): grid layout, or scrolling list for Touch"]
ScreenTex["THREE.CanvasTexture<br/>map + emissiveMap on the screen plane"]
pxToWorld["pxToWorld(px, py)<br/>content px → phone local → world, through phone scale"]
end
subgraph Agent["Agent · state machine (stepAgent), one path per experiment"]
Look["look<br/>head sweep · optic lobes"]
Walk["1 walk<br/>steer to stand point · gait · CX, DN, T1–T3"]
Takeoff["2 takeoff → hover<br/>climb over the prompt, read"]
Dive["2 dive → land<br/>bezier to the tile, landing = answer"]
Scroll["3 scroll<br/>foreleg swipes move scrollY"]
Tap["tap<br/>foreleg reach · press · return"]
React["react"]
Celebrate["celebrate<br/>word complete"]
chooseTile["chooseTile()<br/>P(correct) = 0.35 + 0.6 · memory"]
pickTarget["pickTarget()<br/>tile centre + normal scatter"]
Look --> Walk --> Tap
Look --> Takeoff --> Dive --> React
Look --> Scroll --> Tap
Tap --> React --> Look
Tap --> Celebrate --> Look
Dive --> Celebrate
end
subgraph Records["Trajectories and heatmap"]
Traj["trajGroups[exp]<br/>THREE.Line per trial, green or red"]
Heat["heat[exp] canvas<br/>radial blobs at landing px, drawn into the screen"]
Toggles["Show paths · Show landing heatmap"]
end
subgraph Fly["Procedural fly (animateFly)"]
Body["body group<br/>thorax · abdomen · head · eyes · antennae"]
Wings["wings<br/>buzz on reward"]
Proboscis["proboscis<br/>extends on reward"]
Legs["6 legs · femur + tibia<br/>world-space feet · tripod stepping"]
IK["solveLeg()<br/>two-bone analytic IK"]
FlyState["flyState<br/>pos · yaw · headYaw · buzz · flinch · gaitPhase"]
end
subgraph Brain["Brain activity model"]
Regions["REGIONS[16]<br/>optic L/R · central · MB L/R · CX · AL L/R<br/>LH L/R · SEZ · DN · T1 · T2 · T3 · ABD"]
Act["act[16] Float32Array<br/>pulse(i, v) · exponential decay"]
PointCloud["Points ~55k<br/>ShaderMaterial: region → act[] → colour, size"]
Fibres["FIBRES[14] CatmullRom tracts<br/>pulse particles gated by source activity"]
Groups["GROUPS[7]<br/>anatomical labels for the HUD"]
end
subgraph Render["Rendering · three.js r160"]
MainScene["Main scene<br/>table · phone · glass · fly · lights · shadows"]
MainCam["Perspective camera + OrbitControls<br/>follow / phone modes"]
EyeCam["Eye camera on the head<br/>fov 140"]
EyeRT["WebGLRenderTarget 256²"]
EyeShader["Ommatidia shader quad<br/>hex nearest-centre sampling"]
BrainScene["Brain scene · second renderer<br/>OrbitControls autoRotate"]
Loop["frame()<br/>dt = raw · speed, substepped at 25 ms"]
end
end
CDN["cdn.jsdelivr.net<br/>three.module.js · OrbitControls.js"] -.import map.-> Render
Header -->|speed, camera, play| Loop
Header -->|set camera mode| MainCam
BrainHUD -->|reset| GameState
WORDS --> buildTiles --> GameState
Distractors --> buildTiles
GameState --> ScreenCanvas --> ScreenTex --> MainScene
GameState --> TrialCard
GameState --> BrainHUD
Loop --> Agent
Loop --> Fly
Loop -->|decay| Act
Loop --> MainCam
chooseTile --> pickTarget
GameState --> chooseTile
pickTarget --> pxToWorld
pxToWorld --> FlyState
Walk -->|record positions| Traj
Dive -->|record positions| Traj
registerTap -->|landing px| Heat
Heat --> ScreenCanvas
Toggles --> Traj
Toggles --> Heat
Traj --> MainScene
Agent -->|pulse| Act
Agent -->|phase| StatePills
Tap -->|foot reaches tile| Legs
Tap --> registerTap --> GameState
registerTap -->|pulse| Act
registerTap -->|buzz, proboscis, flinch| FlyState
Celebrate --> nextWord --> GameState
FlyState --> Body
FlyState --> Wings
FlyState --> Proboscis
Legs --> IK --> MainScene
Body --> EyeCam
Regions --> PointCloud
Regions --> Fibres
Act --> PointCloud
Act --> Fibres
Act --> Groups --> BrainHUD
PointCloud --> BrainScene
Fibres --> BrainScene
MainScene --> MainCam --> Loop
MainScene --> EyeCam --> EyeRT --> EyeShader -->|scissor viewport| Loop
BrainScene --> Loop
Agent state machine
stateDiagram-v2
[*] --> look
look: look - head sweeps, optic lobes active, Touch scrolls back to the top
state "Experiment 1 - Walking" as E1 {
walk: walk - turn toward the tile and move to a stand point short of it
}
state "Experiment 2 - Flying" as E2 {
takeoff: takeoff - eased climb to the hover point over the prompt
hover: hover - bob in place, face up the screen, read with a head sweep
dive: dive - quadratic bezier to the landing point, body pitches with velocity
land: land - feet planted, landing registered as the answer
takeoff --> hover
hover --> dive: pick a tile
dive --> land
}
state "Experiment 3 - Touch" as E3 {
scroll: scroll - foreleg swipes until the tile sits at the tap line
}
tap: tap - nearest foreleg reaches, presses the glass and returns, registerTap fires at the press
react: react - short pause, flinch if wrong
celebrate: celebrate - wing buzz, then the next word
look --> walk: experiment 1, pick a tile
look --> takeoff: experiment 2
look --> scroll: experiment 3, pick a tile
walk --> tap: at the stand point, facing the tile
scroll --> tap: tile in reach
tap --> react: word not complete
tap --> celebrate: 3 of 3 placed
land --> look: word not complete
land --> celebrate: 3 of 3 placed
react --> look
celebrate --> look: new word, tiles rebuilt
One answer, end to end
sequenceDiagram
participant L as frame loop
participant A as stepAgent
participant G as game state
participant S as drawScreen / CanvasTexture
participant F as fly (legs, IK)
participant B as act[] / brain
participant H as HUD
L->>A: dt (substepped)
A->>G: chooseTile() reads memory of the needed part
A->>F: stand point from pxToWorld(tile), yaw toward tile
loop each substep while walking
A->>B: pulse(CX, DN, T1–T3)
F->>F: step feet that drift > 0.5 from rest, solve IK
end
A->>F: foreleg foot lerps to the tile, presses the glass
A->>G: registerTap(tileIdx)
G->>G: add landing px to the heatmap, close the trajectory line
alt correct part
G->>G: memory[part] += 0.22·(1−m), dopamine += 0.28, placed++
G->>B: pulse(MB L/R, AL L/R, SEZ)
G->>F: buzz = 0.9, proboscis = 1
else wrong part
G->>G: memory[part] −= 0.03, dopamine −= 0.14
G->>B: pulse(LH L/R, DN)
G->>F: flinch = 1
end
G->>S: flash tile, redraw, texture.needsUpdate
G->>H: renderTrial() chips, progress, feedback, stats, memory
L->>B: decay act[] toward 0.08
L->>H: renderBrainHud() every 70 ms
L->>L: render main scene, eye inset, brain scene
From this app to the full EDLS
flowchart LR
subgraph Now["In this repo today"]
UI["3D language interface<br/>screen · tiles · trial card"]
FlyP["Procedural fly<br/>IK gait, flight, swipe, tap, buzz, flinch<br/>fifteen experiments"]
Policy["Behavioural policy<br/>memory-weighted tile choice"]
BrainViz["Stylised CNS point cloud<br/>16 regions, act[]"]
Eye["Fly-eye mosaic<br/>750 ommatidia, 4.5°"]
end
subgraph Planned["Planned per the TRD"]
MuJoCo["MuJoCo flybody via WebAssembly<br/>800 Hz, adhesion actuators"]
FlyGM["FlyGM connectome controller<br/>MaleCNS v1.0, PAM reward"]
xLSTM["xLSTM + TFLA on WebGPU"]
xAPI["xAPI statements"]
DMZ["Science DMZ · DTN"]
LRS["Learning Record Store"]
Eval["G-DINA · HLR · Rasch · DDT"]
end
Policy -. replaced by .-> FlyGM
FlyP -. replaced by .-> MuJoCo
BrainViz -. fed by .-> FlyGM
Eye -. rendered from .-> MuJoCo
UI --> xAPI --> DMZ --> LRS --> Eval
Eval -. difficulty .-> UI
FlyGM --> xLSTM
Diagrams render with Mermaid when the library loads; otherwise the source is shown.
The laptop experiments
The grades are meta by design: the four student experiments produce the data the teacher reads on the laptop, and the teacher's own navigation becomes a second study, measured with the same care as the students'.
Three of the nine students are seeded each session with one behaviour: looking away from the task, a helper fly in frame, a second device, or answering while off camera. Honest students still glance away now and then, which is where false alarms come from. The admin never sees which students were seeded; the dashboard does, so the flags can be scored.
The classroom
Every fly has to finish the same twelve items, and a lesson ends only when the last one does. In between, the flies behave like flies. A male may orient toward a female, follow her, tap her with a foreleg and sing by vibrating one extended wing. A busy, unreceptive female usually ignores him and keeps tapping, or flicks her wings and kicks, or decamps on a short flight. Each rejection lowers his courtship drive, the courtship-conditioning effect, so the room settles as the lesson goes on. Rival males trade wing threats and lunges, flies groom between trials, a glance at a tablemate who is further ahead makes the next answer more likely to be right, and busy tablemates keep each other on task. Turn mating season off to compare how long the class takes. With twenty-four flies there is no single fly-eye view here; instead a vision overlay, like the district admin's, boxes every fly and its forelegs, draws where it is looking with an eye-contact badge (its own tablet, a neighbour's, or lost to a suitor or rival), and traces its recent path.
Behaviour sources for the classroom
- Connolly, K., & Cook, R. (1973). Rejection responses by female Drosophila melanogaster: their ontogeny, causality and effects upon the behaviour of the courting male. Behaviour, 44, 142–166.
- Siegel, R. W., & Hall, J. C. (1979). Conditioned responses in courtship behavior of normal and mutant Drosophila. PNAS, 76(7), 3430–3434.
- Greenspan, R. J., & Ferveur, J.-F. (2000). Courtship in Drosophila. Annual Review of Genetics, 34, 205–232.
- Chen, S., Lee, A. Y., Bowens, N. M., Huber, R., & Kravitz, E. A. (2002). Fighting fruit flies: a model system for the study of aggression. PNAS, 99(8), 5664–5668.
- Schneider, J., Dickinson, M. H., & Levine, J. D. (2012). Social structures depend on innate determinants and chemosensory processing in Drosophila. PNAS, 109(Suppl. 2), 17174–17179.
- Battesti, M., Moreno, C., Joly, D., & Mery, F. (2012). Spread of social information and dynamics of social transmission within Drosophila groups. Current Biology, 22(4), 309–313.
- Seeds, A. M., et al. (2014). A suppression hierarchy among competing motor programs drives sequential grooming in Drosophila. eLife, 3, e02951.
- Ramdya, P., et al. (2015). Mechanosensory interactions drive collective behaviour in Drosophila. Nature, 519, 233–236.
- Zajonc, R. B. (1965). Social facilitation. Science, 149(3681), 269–274.
The model is a caricature built on these findings, not a fit to their data.
Phonics
A fly has no voice, but male Drosophila do make sound: they vibrate one extended wing to sing a courtship song with two parts, a pulse song whose pulses come about 35 ms apart and a sine song that hums near 150 Hz, and they hear it with the Johnston's organ in the antennae. The phonics experiment borrows that apparatus. Stop consonants are sung as pulse song, three pulses 35 ms apart filtered at the consonant's burst frequency; vowels are a hum shaped by the vowel's first two formants; fricatives are a hiss fluttered at the wingbeat; nasals and liquids are low or gliding hums. You hear the buzz while the fly sings, and only then: a wingbeat hum near 200 Hz with the second wing slightly detuned, coloured by each sound. Each sound is right with a probability set by its letter-sound memory, and wrong ones are the confusions a phonics teacher expects, such as /t/ for /k/ or /s/ for /ʃ/.
Sources for the wing song
- Bennet-Clark, H. C., & Ewing, A. W. (1969). Pulse interval as a critical parameter in the courtship song of Drosophila melanogaster. Animal Behaviour, 17, 755–759.
- von Schilcher, F. (1976). The function of pulse song and sine song in the courtship of Drosophila melanogaster. Animal Behaviour, 24, 622–625.
- Göpfert, M. C., & Robert, D. (2002). The mechanical basis of Drosophila audition. Journal of Experimental Biology, 205, 1199–1208.
- von Philipsborn, A. C., et al. (2011). Neuronal control of Drosophila courtship song. Neuron, 69(3), 509–522.
The mapping from speech sounds to song modes is an invention of this simulation, not a claim about flies.
Reading
The reading app follows the pattern of illustrated readers such as LitLab: a picture and a short passage on each spread, a strip of words to know, and a comprehension check about every three pages. The four books are written for grades 3 to 5 and set in a fruit fly's world, where a peach is a mountain and a kitchen fan is a storm: Rosa and the Ripening Banana (patience and change over time), Flynn and the Giant Peach (exploration), Zig, Zag, and the Wind (cause and effect) and Dot and the Mystery Light (investigation and evidence). Every page is illustrated with an AI-generated image made from that page's prompt, with one consistent fly character; the app loads web-sized copies from docs/stories. If an image is missing, the page is painted in the browser from a scene description instead. The prompts, and how to add or replace images, are in docs/stories/prompts.md.
The fly reads word by word at about 110 words a minute, slowing on the bold vocabulary words. It flies to each word as it reads it: its position is a spring chasing a point just in front of the word, so it carries momentum from word to word, overshoots a little, hops onto each new word and swoops back at the end of a line. Each word is voiced by the same wing buzz as the phonics lesson: a hum shaped by the word's vowels, a pulse at a stop consonant, rising at a question and falling at the end of a sentence. Unfamiliar vocabulary is misread more often, and most misreadings are self-corrected. Stop & Think after page 3 and a comprehension question after page 6 are answered right with a probability that rises with the story's vocabulary memory. These books are structured like decodable readers but are not aligned to a phonics scope and sequence; mapping them to a grade 3 to 5 morphology progression is the next step.
Writing
The writing app asks about twelve Greek roots, alternating between "What does the Greek root bio mean?" and "Which Greek root means light?", with example words such as biology and photograph. The fly answers in joined cursive, traced from the Hershey single-stroke script font, a public-domain set of pen-plotter letters. To write, it flies pitched about 29° nose-up so that the tip of its abdomen rests on the glass, follows each letter's stroke at about 90 pixels a second, and lifts the tip to hop to the dot of an i or the cross of a t. The abdominal ganglion lights up in the brain panel while it writes. A small wobble from hovering flight shakes the line; it shrinks as the fly's tail control improves with practice. When it taps Submit, the recogniser reads the handwriting: very shaky writing can turn one letter into another. Whether the answer itself is right depends on a memory per root, learned with the same Δw = η(R − V) rule.
VR
The fly stands on a ball that floats on a cushion of air, so each step turns the ball instead of carrying the fly anywhere. Its headset is a single curved visor wrapped over both compound eyes, with a glossy faceplate, a light strip, foam gasket and straps. A cable runs from the back of the strap up a tether boom and down to a control box, which is also wired to the ball's tracking and to a laptop that shows what the headset shows. A teacher fly stands on a second, identical rig, and its headset puts it at the front of the same virtual room: it steps along the chalkboard as it writes, with its right foreleg tracing the chalk, then turns to look across the desks. Its laptop shows the class from the teacher's eyes. The student's headset seats it at a desk in a third grade class: sixteen desks, classmates who fidget, windows, and a teacher at a chalkboard. The teacher chalks one item at a time, either a word to build from its parts (re + count + able = ?) or a prefix to define (pre- means ?). The student looks down at its open booklet and writes the answer in cursive pencil, and the fly's right foreleg traces the same strokes in small. The teacher marks it with a tick or a cross, and after six items the board is erased for a new page. Walking bobs the view; now and then the fly turns on the ball and the student glances at a classmate. Answers train the same word part memory as the other experiments.
AR
No treadmill here: the fly really walks, on the table, in a glass headset with a clear visor under a slim frame. The headset overlays a spatial grid on the table, a floating word frame with prefix, root and suffix slots, and six holographic blocks at head height, three carrying the word's parts and three carrying parts of other words. The blocks it is not fetching are obstacles, so it steers around them. It points its right foreleg at a block to pinch it (the headset draws the ray), carries it to the frame, and twists it with a circling foreleg until the label faces out. A right block snaps in green; a wrong one flashes red and drifts back to where it was. The chance of fetching the right block rises with the memory of that part, which learns with every placement.
Scrabble
The fruit fly plays a Blue Morpho on a full 15 by 15 board with the standard premium squares, letter values and a 98-tile bag. The first word goes through the centre star, and every later word must cross one on the board without touching any other; seven tiles in one turn score 50 more. On its turn the fly walks to its rack, takes a tile with a foreleg, walks it to its square and sets it down, one tile at a time; the butterfly flies to its rack, picks a tile up, flies it over and drops it. Each player starts knowing the two- and three-letter words and about half of the rest of the word list, plays its best-scoring word most of the time, and learns seven in ten of the words its opponent plays. Games run until the bag and a rack are empty, and the side panel keeps the score, the racks and the wins. In this experiment the student is always the fruit fly.
Paper book
The same story as in Reading, Flynn and the Giant Peach, with the same pictures, printed as a hardback picture book lying open on the table. The fly looks over the picture on the left page, then flies from word to word above the text on the right, buzzing each word aloud in the same wing voice; the story's vocabulary is printed in bold and read more slowly until it is learned. At the foot of the page it flies to the outer corner, grips it with both forelegs and beats its wings to lift the page, which comes up slowly and curls from the spine with the corner leading. Past upright it lets go, and the page falls over onto the left. After the last page and The End the pages flip back and it reads the book again.
Geography
A globe stands on a school desk, drawn with real country outlines from Natural Earth and tilted on its axis in a brass meridian ring. A trivia card on the desk asks a question, such as which country is home to Machu Picchu or which city is the capital of Poland. The fly walks to the card and reads it, flies to the globe and sweeps it round with a foreleg, then flies to the country it has chosen and taps it. The country lights green if it is right; if it is wrong it lights red and the right country lights green, and the distance between them is measured along the Earth's surface. A memory per country decides how often it is right, and a wrong choice is usually one of the right country's neighbours. Questions come in rounds of ten.
Butterfly mode
The two logos at the left of the header switch the insect. In butterfly mode the fly becomes a Blue Morpho, 1.8 times the fly's size, with iridescent blue wings with a black margin and white spots, brown with eyespots underneath. The classroom becomes six species common around Gainesville, Florida: Zebra Longwing, Gulf Fritillary, Monarch, Eastern Tiger Swallowtail, Cloudless Sulphur and Common Buckeye, four of each, mixed across the tables. The brain and behaviour are unchanged, but the big wings change the movement. At rest they are held closed over the back and opened now and then to bask; in flight they beat about four times a second instead of two hundred, and each downstroke lifts the body so the flight bobs and weaves.
From this app to the full EDLS
In the browser today
Procedural body and behaviour, a memory-weighted policy, a stylised brain, the fly-eye mosaic, the curriculum, and the four dashboards running on live telemetry from the scene.
Planned per the requirement document
The MuJoCo flybody model compiled to WebAssembly at 800 Hz with 102 degrees of freedom and adhesion actuators; the Fly-connectomic Graph Model on the MaleCNS v1.0 connectome (166,691 neurons, more than 125 million synapses) as the controller; xLSTM with Tiled Flash Linear Attention on WebGPU; xAPI telemetry through a Science DMZ into a Learning Record Store.
Research library
The five reports behind the design, with page images from each. Every source they cite is listed below.
Re-Engineering the Connectome
Google DeepMind’s fruit fly brain mapping, embodied physics simulation, and the repurposing of insect neural circuits for complex tasks. 14 pages.
Figures: the connectome-to-controller pipeline and the repurposed-circuit task table.
Neural Connectomics in Silicon
High-fidelity simulation, embodied control, and whole-brain modelling of Drosophila melanogaster. 15 pages.
Figures: neurotransmitter inference and the signed adjacency formulation; leaky integrate-and-fire dynamics.
Embodied AI and Cognitive Psychometrics
Evaluating a simulated Drosophila connectome in a web-based language acquisition environment. 17 pages. The source of the Q-matrix, G-DINA, Half-Life Regression, Rasch and Decision Transformer methods used in the dashboards.
Figures: the Q-matrix and the DINA and G-DINA item response functions; the Half-Life Regression and Rasch specifications.
Architectural Analysis of Open-Source Language Learning Systems
Duolingo-style frameworks, morphological engines and pedagogical corpora: LibreLingo, Word Root Workshop, LEAF, MorphoLex. 12 pages.
Figure: platform comparison table.
EDLS Technical Requirement Document
The requirement document this app implements the interaction layer of: body, connectome controller, interface optics, evaluation, telemetry. 4 pages.
Sources cited in the reports
Titles and links as they appear in each report's works-cited list, 187 in all. Links open in a new tab.
Re-Engineering the Connectome35 sources
- A connectomics milestone: Mapping the complete male fruit fly brain https://research.google/blog/a-connectomics-milestone-mapping-the-complete-male-fruit-fly-brain/
- FlyWire https://edit.flywire.ai/
- flybody : fruit fly body model for MuJoCo physics - GitHub https://github.com/TuragaLab/flybody
- World-first male fly brain map helps unlock secret workings of the mind https://www.zoo.cam.ac.uk/news/world-first-male-fly-brain-map-helps-unlock-secret-workings-mind
- FlyWire: How A Complete Model of a Fruit Fly's Brain is https://www.fmai-hub.com/flywire-how-a-complete-model-of-a-fruit-flys-brain-is-transforming-neuroscience/
- Whole-body simulation of realistic fruit fly locomotion with deep https://www.biorxiv.org/content/10.1101/2024.03.11.584515v1-0
- Fruit Fly Brain Uploaded to AI: How the Connectome Project Works https://www.mindstudio.ai/blog/fruit-fly-brain-connectome-ai-training
- GitHub - cobanov/awesome-fly: A curated list of fruit fly connectome https://github.com/cobanov/awesome-fly
- GitHub - MarkUnthank/flyhard: A connectome-based fruit fly https://github.com/MarkUnthank/flyhard
- GitHub - nftechie/doomfly: Fly-connectome simulation controlling a https://github.com/nftechie/doomfly
- Scientists Complete Full Map of the Fruit Fly Central Nervous System https://www.hhmi.org/news/scientists-complete-full-map-fruit-fly-brain-connectome
- A connectome and analysis of the adult Drosophila central brain https://edoc.mdc-berlin.de/id/eprint/19361/1/19361oa.pdf
- Google Open-Sourced A Fly Brain. Ten Reasons That Have Nothing https://theagiclock.com/articles/google-fly-connectome-ten-reasons
- How scientists mapped a fruit-fly brain | DijitalPi https://dijitalpi.com/en/research/academic/how-scientists-mapped-a-fruit-fly-brain-neuron-by-neuron/
- A connectomics milestone: Mapping the complete male fruit fly brain https://3quarksdaily.com/3quarksdaily/2026/09/a-connectomics-milestone-mapping-the-complete-male-fruit-fly-brain.html
- See a map of the male fruit fly brain - Google Blog https://blog.google/innovation-and-ai/technology/research/male-fruit-fly-brain-map/
- Feng Li's research works | Howard Hughes Medical Institute, Chevy https://www.researchgate.net/scientific-contributions/Feng-Li-2163040304
- Sexual dimorphism in the complete connectome of the Drosophila https://www.researchgate.net/publication/396402625_Sexual_dimorphism_in_the_complete_connectome_of_the_Drosophila_male_central_nervous_system
- simulating embodied sensorimotor control in adult Drosophila https://www.researchgate.net/publication/385743110_NeuroMechFly_v2_simulating_embodied_sensorimotor_control_in_adult_Drosophila
- Data-driven development, applications, and modularity of https://www.researchgate.net/figure/Data-driven-development-applications-and-modularity-of-NeuroMechFly-a-Body-structures_fig1_360529556
- Artificial intelligence brings a virtual fly to life https://www.janelia.org/news/artificial-intelligence-brings-a-virtual-fly-to-life
- Whole-body simulation of realistic fruit fly locomotion with deep https://www.biorxiv.org/content/10.1101/2024.03.11.584515v2.full-text
- GitHub - google-deepmind/mujoco_menagerie: A collection of high https://github.com/google-deepmind/mujoco_menagerie
- NeuroMechFly 2.0, a framework for simulating embodied ... - bioRxiv https://www.biorxiv.org/content/10.1101/2023.09.18.556649v2.full-text
- Whole-Brain Connectomic Graph Model Enables Whole-Body https://arxiv.org/html/2602.17997v1
- Researchers Upload Fly's Brain to Matrix, Let It Control Virtual Body https://futurism.com/science-energy/research-fly-brain-matrix
- (PDF) Neural Circuit Architectural Priors for Quadruped Locomotion https://www.researchgate.net/publication/384811700_Neural_Circuit_Architectural_Priors_for_Quadruped_Locomotion
- Scientists achieve first “virtual brain” upload with digital fruit fly replica https://weeklyblitz.net/2026/03/11/scientists-achieve-first-virtual-brain-upload-with-digital-fruit-fly-replica/
- Inhibitory Neurons Drive Rhythmic Movement - Neuroscience News https://neurosciencenews.com/inhibitory-neurons-rhythmic-movement-30524/
- Fruit Fly Brain Email Classifier: Inside the Connectome Experiment https://www.mindstudio.ai/blog/how-to-train-fruit-fly-brain-email-classifier
- (PDF) Whole-Brain Connectomic Graph Model Enables Whole-Body https://www.researchgate.net/publication/401057810_Whole-Brain_Connectomic_Graph_Model_Enables_Whole-Body_Locomotion_Control_in_Fruit_Fly
- How Google DeepMind's Virtual Fruit Fly Could Reshape Science https://medium.com/@aitechtoolbox48/how-google-deepminds-virtual-fruit-fly-could-reshape-science-and-technology-395a2fa97a42
- The Blank Architecture: On De Novo Connectomes, Virtual ... - Medium https://medium.com/@hfwnbq/the-blank-architecture-on-de-novo-connectomes-virtual-environments-and-the-development-of-mind-59cc88d078c8
- Computing with Fruit Fly Brain Circuits - Bionet Group http://www.bionet.ee.columbia.edu/research/dnip
- The First Multi-Behavior Brain Upload (of a fruit fly) : r/consciousness https://www.reddit.com/r/consciousness/comments/1roea5h/the_first_multibehavior_brain_upload_of_a_fruit/
Neural Connectomics in Silicon32 sources
- Google's Simulated Fruit Fly AI Learns to Drive, Park & Game | Stork.AI https://www.stork.ai/blog/this-ai-fly-drives-better-than-you
- A Drosophila computational brain model reveals sensorimotor https://www.repository.cam.ac.uk/items/f6d6fd8e-ffa0-4e9f-aaca-71732c523253
- Researchers simulate an entire fly brain on a laptop. Is a human https://news.berkeley.edu/2024/10/02/researchers-simulate-an-entire-fly-brain-on-a-laptop-is-a-human-brain-next/
- The Adult Drosophila Connectome Ecosystem https://flyconnecto.me/2026/09/04/the-adult-drosophila-connectome-ecosystem/
- Google maps entire brain and central nervous system of adult male https://www.tomshardware.com/software/programming/google-maps-entire-brain-and-central-nervous-system-of-adult-male-fruit-fly-software-engineers-immediately-make-it-run-doom-ai-powered-3d-model-of-over-166-000-neurons-can-also-play-super-mario-64
- Three Days After Google Mapped a Fly Brain, It Plays Doom - AIxploria https://www.aixploria.com/en/ai-radar/fly-brain-connectome-doom-mario-64/
- GitHub - nftechie/doomfly: Fly-connectome simulation controlling a https://github.com/nftechie/doomfly
- Simulating embodied sensorimotor control with NeuroMechFly v2 https://github.com/NeLy-EPFL/flygym/
- NeuroMechFly v2: Simulating how fruit flies see, smell, and navigate https://www.eurekalert.org/news-releases/1064414
- Whole-Brain Connectomic Graph Model Enables Whole-Body https://arxiv.org/html/2602.17997v3
- NeuroMechFly https://neuromechfly.org/api_reference/flygym/compose/fly/neuromechfly/
- NeuroMechFly documentation - GitHub Pages https://nely-epfl.github.io/flygym-gymnasium/
- GitHub - cobanov/awesome-fly: A curated list of fruit fly connectome https://github.com/cobanov/awesome-fly
- Compiling a Vector Programming Language to the Drosophila https://clawrxiv.io/abs/2604.01566
- Can a fruit fly run a company better than I can? - Buğra Tezcan https://bugratezcan.com/posts/fruit-fly-ceo/
- (PDF) A Drosophila computational brain model reveals sensorimotor https://www.researchgate.net/publication/384573114_A_Drosophila_computational_brain_model_reveals_sensorimotor_processing
- NeuroMechFly https://neuromechfly.org/
- After Google mapped an adult male fruit fly's brain, software ... - Reddit https://www.reddit.com/r/gaming/comments/1wcfks2/after_google_mapped_an_adult_male_fruit_flys/
- Prediction of neural activity in connectome-constrained recurrent https://www.researchgate.net/publication/396947961_Prediction_of_neural_activity_in_connectome-constrained_recurrent_networks
- Whole-Brain Connectomic Graph Model Enables Whole-Body https://arxiv.org/html/2602.17997v1
- Infrequent strong connections constrain connectomic predictions of https://www.biorxiv.org/content/10.1101/2025.03.06.641774.full
- Fruit Fly Brain Uploaded to AI: How the Connectome Project Works https://www.mindstudio.ai/blog/fruit-fly-brain-connectome-ai-training
- GitHub - MarkUnthank/flyhard: A connectome-based fruit fly https://github.com/MarkUnthank/flyhard
- cobanov/flyjump: Fly Dino: original Chromium Dino ... - GitHub https://github.com/cobanov/flyjump
- The Latest Weird Thing to Play Doom Is the Mapped-Out Brain of a https://www.cnet.com/tech/gaming/simulated-fruit-fly-brain-plays-doom/
- DOOM: A 166,700-Neuron Simulated Fruit Fly Brain Has Learned to https://retrogems.fr/en/simulated-fruit-fly-brain-plays-doom/
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Embodied AI and Cognitive Psychometrics85 sources
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- A Trainable Spaced Repetition Model for Language Learning https://research.duolingo.com/papers/settles.acl16.pdf
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Architectural Analysis of Open-Source Language Learning Systems35 sources
- LibreLingo - Open source language learning platform https://szabgab.com/librelingo
- Vuizur/awesome-language-learning - GitHub https://github.com/Vuizur/awesome-language-learning
- kantord/LibreLingo: a community-owned language-learning ... - GitHub https://github.com/kantord/LibreLingo
- LibreLingo a community-owned language-learning platform - GitHub https://github.com/LibreLingoCommunity/LibreLingo
- LibreLingo/Dockerfile at main - GitHub https://github.com/LibreLingoCommunity/LibreLingo/blob/main/Dockerfile
- When redoing a "word picking" task, words go missing #25 - GitHub https://github.com/LibreLingo/LibreLingo-ES-from-EN/issues/25
- GitHub - aman-lj/LibreLingo-ZH-fron-EN https://github.com/aman-lj/LibreLingo-ZH-fron-EN
- GitHub - LibreLingoCommunity/course-template https://github.com/LibreLingoCommunity/course-template
- Open-source tool for building courses like Duolingo - Reddit https://www.reddit.com/r/SideProject/comments/17y2bfb/opensource_tool_for_building_courses_like_duolingo/
- Open-source tool for Duolingo-like courses - Indie Hackers https://www.indiehackers.com/post/open-source-tool-for-duolingo-like-courses-fcb872376e
- GitHub - zoonk/uneebee: Platform for creating interactive courses. https://github.com/zoonk/uneebee
- zoonk repositories - GitHub https://github.com/orgs/zoonk/repositories
- Zoonk - GitHub https://github.com/zoonk
- LEAF: Full-stack Duolingo Clone Web Application - GitHub https://github.com/SudoKMaar/leaf
- Davronov-Alimardon/duolingo-clone - GitHub https://github.com/Davronov-Alimardon/duolingo-clone
- sanidhyy/duolingo-clone: Lingo - Interactive platform for language https://github.com/sanidhyy/duolingo-clone
- joeseesun/word-root-workshop: 词根词缀记忆工坊 - GitHub https://github.com/joeseesun/word-root-workshop
- Match Game https://hebrews110.github.io/prefixSuffixThird/
- Match Game - GitHub Pages https://hebrews110.github.io/prefixSuffix/
- jacobprall/CrossWords: MERN Stack Game - GitHub https://github.com/jacobprall/CrossWords
- benn-mctosh/words: game for grandma - GitHub https://github.com/benn-mctosh/words
- UmerTariq1/WordBox-Solver-for-Plato: Given a boggle grid ... - GitHub https://github.com/UmerTariq1/WordBox-Solver-for-Plato
- Curious Learning - Digital Public Goods Backlog https://backlog.digitalpublicgoods.net/curiouslearning/
- Curious Learning - GitHub https://github.com/curiouslearning
- Activity · curiouslearning/FeedTheMonster - GitHub https://github.com/curiouslearning/FeedTheMonster/activity
- Software:GCompris - HandWiki https://handwiki.org/wiki/Software:GCompris
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- Education / GCompris Data · GitLab - KDE Invent https://invent.kde.org/education/gcompris-data
- hugomailhot/MorphoLex-en: Lexical database for - GitHub https://github.com/hugomailhot/MorphoLex-en
- GitHub - hugomailhot/morpholex-fr: Lexical database for https://github.com/hugomailhot/morpholex-fr
- MorphoLex: A derivational morphological database ... - ResearchGate https://www.researchgate.net/publication/320987123_MorphoLex_A_derivational_morphological_database_for_70000_English_words
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- A derivational morphological database for 70000 English words https://pubmed.ncbi.nlm.nih.gov/29124719/
- WithEnglishWeCan/generated-english-roots-list - GitHub https://github.com/WithEnglishWeCan/generated-english-roots-list
- droher/etymology-db - GitHub https://github.com/droher/etymology-db
Design and code
Styling uses a University of Florida palette: core blue for actions, dark blue for chrome and text, alachua for work in flight, gator green for mastery, and bottlebrush red only for errors. Headings and controls are set in Neulis Sans and running text in Liebling, both with system fallbacks. The simulation is a single HTML file on three.js. Source on GitHub.