This repository contains the official trained policies for The files are organized by task environment and training seed: The play.py script in the JoSE repository downloads a pretrained model and executes it in the corresponding environment, allowing agent behaviour to be visualized. The script is called from the repo root as follows: Seeds vary by hand and task. See the HuggingFace model directory to see which seeds are available for each task-hand combination.
Open-weight model · Reinforcement learning
ganglion-haltere-cursor
by Artem Skulimovskiy Skulitom/ganglion-haltere-cursor
Two checkpoints of the Haltere fly-brain connectome (a 30,000-neuron recurrent network with the connectome's structure and signs, flight-trained) with a linear motor readout that turns the network's motor-neuron rates into a cursor or view velocity, trained…
Parameters—
Context—
Weights23.1 MB
Licensemit
AccessOpen weights
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Model Card
By Artem Skulimovskiy, published under mit, revision e982f015b692.
Two checkpoints of the Haltere fly-brain connectome (a 30,000-neuron recurrent network with the connectome's structure and signs, flight-trained) with a linear motor readout that turns the network's motor-neuron rates into a cursor or view velocity, trained by imitation of a proportional controller in Ganglion, the reflex layer that runs them against live applications at 100 Hz. The report-.json files beside them are the training and suite reports they were selected from. Everything here was measured on one machine (RTX 4090, Windows 11); the numbers are the suite's and the live harness's, with their caveats, and are documented in full in the repository's TRAINING.md and HALFLIFE.md. The…
Read Artem Skulimovskiy's full model card
Ganglion cursor readouts on the Haltere connectome
Two checkpoints of the Haltere fly-brain connectome (a 30,000-neuron recurrent network with the connectome's structure and signs, flight-trained) with a linear motor readout that turns the network's motor-neuron rates into a cursor or view velocity, trained by imitation of a proportional controller in Ganglion, the reflex layer that runs them against live applications at 100 Hz.
| File | What it is | Where it stands |
|---|---|---|
cursor-readout-v6.pt |
Adapter version 5 (the goal as a unit direction plus a tanh distance, goal scale 0.1), all 3,913 motor neurons, three DAgger rounds | The live configuration. Under the reflex layer's supervising envelope it matches the reference controller on the fixed suite (settle 32/32 in 335 ms, jump 256/256, pursuit 31/32 at 5.1 px, camera 32/32 at 3.7 px) and tracked tightest in Half-Life. Alone it cannot stop: it holds speed through the goal and oscillates (0/32 settled). |
cursor-readout-v3b.pt |
Adapter version 3 (goal error over 0.3 s of intent speed), all motor neurons, one DAgger round | The checkpoint that stands on its own: alone it settles 19/32 static targets at 14.8 px; under supervision settle 32/32 in 375 ms with the envelope intervening on 1.9% of steps. Slower to the goal than v6. |
The report-*.json files beside them are the training and suite reports they were selected
from. Everything here was measured on one machine (RTX 4090, Windows 11); the numbers are the
suite's and the live harness's, with their caveats, and are documented in full in the
repository's TRAINING.md
and HALFLIFE.md.
What the model does and does not do
The network is frozen. The readout is a ridge regression from the motor neurons' rates to a two-dimensional velocity in units of the intent's speed, fitted on synthetic cursor episodes (random plants with input delay, gain and speed) to the commands of a memoryless proportional controller, then refined with DAgger on the student's own rollouts. The runtime feeds the network the goal error and, for version 6 checkpoints, nothing else; the readout's proposal is accepted by a supervising envelope only if it moves the cursor toward the goal within the intent's speed limit, otherwise the deterministic reference acts for that tick.
Measured, not claimed:
- Under supervision, v6 reaches its first shot in a live Half-Life fight as fast as the reference controller at the same step limit (0.99 against 0.97 s median over twenty trials, p 0.30) and with the lowest tracking error of the align steps of any condition (121 px against 159, p 0.005). It does not make the chain faster than the reference.
- Alone, no checkpoint here beats the reference. v3b settles 19/32; v6 settles none. A linear readout of this network can hold speed or stop, not both: the network answers a reversal of the goal direction in 60 to 70 ms and its state after a fast approach is not linearly distinguishable from its state during it.
- Every live trial before 15:00 on 2026-09-18 ran with a stray virtual controller holding a move-backward input in the game; the comparisons between conditions stand, the absolute figures describe a fight fought against a wall.
Loading
The checkpoints are standard torch.save files of the Haltere ConnectomeRNN with the
readout installed and a ganglion_cursor metadata record (adapter version, goal scale, the
training options and the seeds). They need the haltere package to load and CUDA to run in
real time:
from ganglion.brain.haltere_cursor import HaltereCursor # from the Ganglion repository
model = HaltereCursor("cursor-readout-v6.pt") # reads the adapter version and goal scale from the file
ganglion core --shadow-checkpoint cursor-readout-v6.pt --shadow-process runs it as the
reflex layer's shadow predictor; ganglion.train.suite scores it on the fixed suite.
Provenance
Base checkpoint: Haltere ftPath2_best.pt (the flight-trained connectome). Training scripts:
ganglion.train.cursor_readout and ganglion.train.cursor_dagger at the commits recorded in
the reports. License: MIT, as the repositories.
Identity and Version
- Repository
- Skulitom/ganglion-haltere-cursor
- Publisher
- Artem Skulimovskiy
- Task
- Reinforcement learning
- Modality
- Control
- Library
- ganglion
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- e982f015b6926a835f222470e3d92f1c8ec42faa
- First published
- 2026-09-18
- Last updated
- 2026-09-18
Files and Weights
8 files, 23.3 MB in total. The weights are 2 files totalling 23.1 MB in pt.
Weights2 files · 23.1 MB
Configuration4 files · 135.4 KB
Documentation1 file · 4.6 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| cursor-readout-v3b.pt | Weights | 11.6 MB | 6a563b3699e6 |
| cursor-readout-v6.pt | Weights | 11.6 MB | e91498772d4a |
| report-dagger-v3b.json | Configuration | 8.6 KB | — |
| report-dagger-v6.json | Configuration | 13.0 KB | — |
| report-suite-v3b.json | Configuration | 57.2 KB | — |
| report-suite-v6.json | Configuration | 56.6 KB | — |
| README.md | Documentation | 4.6 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 23.1 MB
Download from Artem Skulimovskiy
Released by Artem Skulimovskiy through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 23.1 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About ganglion-haltere-cursor
Can I use ganglion-haltere-cursor commercially?
Yes. ganglion-haltere-cursor is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
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