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so yeah I plug the fly brain data to simulate an autonomous driving model for Skypad, the viral Philippine transport vehicle real fly wiring + simulated neurons + assisted avoidance meet flypad autonomous driving cooked using openclaude harness with Astra

Kevin

13,730 subscribers

16,403 просмотров • 4 дней назад •via X (Twitter)

Anya Rossi• Live Now

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Комментарии: 15

Фото профиля BIGCOOKS 😈| 個黑人的中國男性

BIGCOOKS 😈| 個黑人的中國男性4 дней назад

any plans for this ? We saw you buying the coin? 0x97221df4de771b9f8b31b6177f8af1858becea76

Фото профиля 底部筹码 | Golden.S

底部筹码 | Golden.S4 дней назад

0x97221df4de771b9f8b31b6177f8af1858becea76 Sir, I see you purchased this token, and it's still on your own platform. What are your plans?

Фото профиля Kevin

Kevin4 дней назад

This experiment runs 166,700 modeled neurons connected by measured fruit-fly wiring. Its driver-view image stimulates sensory neurons, and two neural readouts contribute to steering. Authored driving assistance predicts the pedestrians and blockers, follows the road, and adjusts steering and speed. It can brake and reverse if needed. The fly’s neural output remains an input to that controller. People and limbs use authored animation.

Фото профиля Kevin

Kevin4 дней назад

Technical details of the Skypad experiment 🪰🇵🇭 • Neural graph: 166,700 retained neurons, 25,582,938 directed weighted connections, representing 124,177,617 synaptic contacts from MaleCNS v1.0. These are our imported graph totals. The wiring includes brain and ventral nerve cord. • Dynamics: approximate leaky integrate-and-fire neurons, simulated locally with Python + a native C++ CPU kernel. Integration timestep: 0.1 ms. Membrane/synaptic time constants: 20/5 ms. Transmission delay: 1.8 ms. • Vision: a dedicated 64×48 driver camera supplies luminance to 3,335 mapped R1–R6 visual neurons. This uses an approximate retinal mapping. The spectator camera does not affect neural input. • Control loop: every 50 ms (20 Hz), the retained neural graph advances and an engineered decoder converts left/right DNp20 spike rates into a steering signal. • Assistance: hand-coded software handles road following, obstacle avoidance, speed, braking and recovery. It has direct access to road/obstacle geometry and evaluates 10 candidate lateral offsets over a 3-second horizon. The neural steering term added to road-following steering is capped at ±0.05 radians (~2.9°). • Vehicle/world: Three.js rendering, simplified planar bicycle dynamics, assisted upright balance, a 270 m road, 10 animated pedestrians and 10 blockers. • Recorded result: 254.813 m in 60 simulated seconds, 19 encounters cleared, zero modeled collisions. Recording took 63.129 seconds on the development machine. • Verification: saved validation reproduced neural spike-count hashes and decoded controls for all 1,200 intervals on the original platform. Neural-input interventions changed the assisted trajectory. This demonstrates measured fly wiring with approximate neural dynamics contributing to assisted driving. No training, reinforcement learning or synaptic plasticity is used. Avoidance skill comes from the software assistance. The video replays a recorded closed-loop run.

Фото профиля 农民

农民4 дней назад

have token can support?

Фото профиля 农民

农民4 дней назад

how support it

Фото профиля Bernadette

Bernadette4 дней назад

The saging in the cart is so funny

Фото профиля Kevin

Kevin4 дней назад

😂😂😂

Фото профиля Bernadette

Bernadette4 дней назад

The neighborhood is giving Bulacan or Cavite vibe haha

Фото профиля Secretpika (1 ETH era)

Secretpika (1 ETH era)4 дней назад

fuk look another oen @BennyInHerBag pinoy spotted LMEOW

Фото профиля David ⚠️

David ⚠️4 дней назад

Wild stack — fly connectome → control signal → Three.js vehicle loop. We’re on the other side of browser 3D with agents that live in the scene, not only scenes that simulate biology. Different problem, same bet that spatial runtimes matter.

Фото профиля Beyong

Beyong4 дней назад

🔥🔥🔥

Фото профиля Kevin

Kevin4 дней назад

theres a lot we can on this fly simulations, this could be a good game or something

Фото профиля Oxdirss

Oxdirss4 дней назад

哇哦第一只会骑摩托车的苍蝇？税给作者

Фото профиля DiFors

DiFors4 дней назад

@off_god_off cought a fly too

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.Kyle Conner makes some good points that the Cybercab is (obviously) designed for autonomous driving, and not manual driving: "It would be kind of bad to drive because you have massive A-pillars. The windshield doesn't come up very high. You sit way too far back, so clearly this is not designed to be a driven vehicle. I thought maybe the early ones would ship with a steering wheel. You're not driving this thing with A-pillars like that."

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For the first time ever, a fruit fly brain has been trained to launch a memecoin. Not "AI inspired by a brain" — the real connectome. 165,122 neurons, 10,228,000 synaptic connections, every one traced from an actual male Drosophila by electron microscopy. Janelia FlyEM + Google Research, released CC-BY. How it works: Screenshots of the live launchpad go in through 892 retinotopic hex columns into L1 and L2 — the neurons sitting directly behind a fly's photoreceptors. All 165,122 integrate. The cursor comes back out of the descending neurons a fly actually walks with: DNa02's left-right asymmetry steers, DNa01 drives forward, DNp09 clicks. The wiring is never trained. Only synaptic gains — you don't get to invent connections the fly doesn't have. Proof its eye is doing real work: in light mode it filled 0 of 3 form fields. In dark mode, 2 of 3. Its retina is a luminance map, so invert the contrast and it goes blind. And the honest split: the fly types the description and the ticker. The rig does the name, the pair and the tax. CA: 0x4eb990547bce4a982432ca88cf5fae7eed1a2d35

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Fruit fly dev

756,578 просмотров • 5 дней назад

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