Publish W26 (repackaged from AlonSaguy/ephys-atlas-models)
Browse files- LICENSE +6 -0
- README.md +62 -0
- autoencoder.pt +3 -0
- checksums.json +50 -0
- ephysatlas_model.json +51 -0
- example/expected_latents.npy +3 -0
- example/units_sample.npz +3 -0
- meta.yaml +6 -0
- point_transformer_gmm.pt +3 -0
- shared_latent_scaler.joblib +3 -0
- split.json +861 -0
- unconditional_gmm_train_only.joblib +3 -0
LICENSE
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Creative Commons Attribution 4.0 International (CC-BY-4.0)
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You are free to share and adapt this material for any purpose, including commercially,
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provided you give appropriate credit.
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Full licence text: https://creativecommons.org/licenses/by/4.0/legalcode
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README.md
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---
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license: cc-by-4.0
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library_name: pytorch
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tags:
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- neuroscience
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- electrophysiology
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- neuropixels
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- unit-level-encoder
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- international-brain-lab
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---
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# Ephys Atlas unit-level encoder (2026_W26)
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A **per-unit** (per spike-sorted neuron) model, in two stages: a multimodal autoencoder embeds a
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unit's multi-channel **waveform** and its **autocorrelogram** into a 32-d latent -- its
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phenotype -- and a Point-Transformer Gaussian mixture over those latents whose components read as
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**putative cell types**. Trained by the
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[International Brain Laboratory](https://www.internationalbrainlab.com/) on release `2026_W26`.
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> **This model encodes; it does not `predict(df)`.** Its input is a unit's waveform + ACG arrays
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> and its output is a point in a learned latent space, not a table of named predictions. So the
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> interface is `encode` (and `reconstruct` / `components` / `assign`), not the `predict(df)` the
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> region classifier and spatial encoder expose.
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> **Requires an IBL/ONE account for atlas-wide use.** Unlike the other families, the recorded unit
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> dataset is **not** shipped here -- it is pulled from IBL S3 via ONE at first use and cached
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> locally. Encoding your *own* units needs only the weights below; reproducing the atlas-wide
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> latents (`.latents()`) triggers the S3 fetch.
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## Quickstart
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```python
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import numpy as np
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from ephysatlas import load_pretrained
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model = load_pretrained("int-brain-lab/ea-encoder-unit", revision="2026_W26")
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z = model.encode(waveform, acg) # [n_units, 32] latent phenotype
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rec = model.reconstruct(waveform, acg) # waveform + ACG reconstruction
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means, log_var = model.components() # GMM putative cell types
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```
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`load_pretrained` is the entry point for every ephysatlas model -- it reads
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`ephysatlas_model.json` and returns the right wrapper.
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## What ships here
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- `autoencoder.pt` -- the multimodal autoencoder.
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- `point_transformer_gmm.pt` -- the Point-Transformer GMM.
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- the latent `StandardScaler`, and the unconditional GMM baseline.
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The recorded per-unit dataset (waveforms, ACGs, positions) is **not** here: it is fetched from S3
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via ONE, keeping this repository weights-only.
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## Reproducibility
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**Pin the revision.** `revision="2026_W26"` is an immutable tag. Verify your install reproduces
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the shipped output with `model.selftest()` (encode-only -- no S3, no account).
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## Citation
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Please cite the International Brain Laboratory Ephys Atlas. Model id `ea-encoder-unit`, vintage
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`2026_W26`.
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autoencoder.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:2442b21cf345c7ff425049112ec9e3a230dde5d5060395eabcc5587d943862f9
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size 85357683
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checksums.json
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ephysatlas_model.json
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{
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"model_id": "ea-encoder-unit",
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"task": "unit-encoding",
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"model_class": "MultimodalAutoencoder",
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"vintage": "2026_W26",
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"training": {
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"random_seed": 0
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},
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"environment": {
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"python": "3.12.10",
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"xgboost": "3.4.0",
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"scikit_learn": "1.8.0",
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"numpy": "2.4.6",
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"pandas": "3.0.5",
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"torch": "2.13.0",
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"ephysatlas": "0.7.0"
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},
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"method": "gmm",
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"granularity": "unit",
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"artifacts": {
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"autoencoder": "autoencoder.pt",
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"pt_gmm": "point_transformer_gmm.pt",
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"scaler": "shared_latent_scaler.joblib",
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"unconditional_gmm": "unconditional_gmm_train_only.joblib"
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},
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"inputs": {
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"index": [
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"pid",
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"cluster"
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],
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"modalities": [
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"waveform",
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"acg"
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]
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},
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"outputs": {
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"kind": "latent",
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"latent_dim": 32
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},
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"config": {
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"architecture": {
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"shared_latent_dim": 32,
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"gmm_components": 16
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}
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},
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"data_source": {
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"backend": "s3-ibl",
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"project": "ibl_neuropixel_brainwide_01",
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"requires_one": true
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}
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}
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example/expected_latents.npy
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version https://git-lfs.github.com/spec/v1
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oid sha256:f810c95d6f4cb7f692157bac7a0bf43044739082400403fc981f38e33c5d4600
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size 64128
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example/units_sample.npz
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version https://git-lfs.github.com/spec/v1
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oid sha256:da9edf4d281d201cabba094f3c0cd3bccb848494dd089b032eae973151e623c8
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size 9140508
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meta.yaml
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MODEL_CLASS: MultimodalAutoencoder
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VINTAGE: 2026_W26
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LATENT_DIM: 32
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GMM_COMPONENTS: 16
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PROJECT: ibl_neuropixel_brainwide_01
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RANDOM_SEED: 0
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point_transformer_gmm.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:b2777aac43926bd5d7c8256b5133ccb138ae7932a3df1b686d80eacf76c2858b
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size 4092837
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shared_latent_scaler.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:f3bd83ea87bb4731402216679b1ea71da41eb563b8b683190b9f23dc50e651e5
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size 1383
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split.json
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|
| 1 |
+
{
|
| 2 |
+
"format_version": 1,
|
| 3 |
+
"split_unit": "probe_insertion_pid",
|
| 4 |
+
"train_pids": [
|
| 5 |
+
"00a824c0-e060-495f-9ebc-79c82fef4c67",
|
| 6 |
+
"00c425fd-ec3e-4cd2-b8af-c0bc0c4bdd44",
|
| 7 |
+
"0179fc61-0a82-4e2b-b6e6-8b44efd23744",
|
| 8 |
+
"0228bcfd-632e-49bd-acd4-c334cf9213e9",
|
| 9 |
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"0252da71-5f32-4d11-b25a-2032ef5f9793",
|
| 10 |
+
"02cb667a-f2e7-4e14-bbaa-ee9996746f5b",
|
| 11 |
+
"02cc03e4-8015-4050-bb42-6c832091febb",
|
| 12 |
+
"0342d5c4-813d-4a47-ada0-563b26189c0c",
|
| 13 |
+
"0393f34c-a2bd-4c01-99c9-f6b4ec6e786d",
|
| 14 |
+
"04c140d4-9782-4f85-8636-75c98ff6f326",
|
| 15 |
+
"04c9890f-2276-4c20-854f-305ff5c9b6cf",
|
| 16 |
+
"05ccc92c-fcb0-4e92-84d3-de033890c7a8",
|
| 17 |
+
"05ec6af9-6c83-422d-91a3-ba815fa65e92",
|
| 18 |
+
"06787c50-ae12-4c3a-a7fa-06d68d053a2c",
|
| 19 |
+
"0685d767-80a2-49b6-a51a-0e9b1dd7a14d",
|
| 20 |
+
"069c2674-80b0-44b4-a3d9-28337512967f",
|
| 21 |
+
"06b677d6-b074-4444-bd4a-3235a310e6e0",
|
| 22 |
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"06c6741e-9f2a-4114-8ca9-0dc5ccc951e6",
|
| 23 |
+
"06d42449-d6ac-4c35-8f85-24ecfbc08bc1",
|
| 24 |
+
"07abb39b-063b-4ef8-b3ab-963c6a1b5cc5",
|
| 25 |
+
"082187a0-121f-47d5-962a-a9ad467e7124",
|
| 26 |
+
"08ed0b3c-9f94-4c1f-8522-3d42a642a6b0",
|
| 27 |
+
"0909252c-3ad0-413f-96f5-7eff885b50aa",
|
| 28 |
+
"098b405f-0e30-4952-85db-61cc62e5a9fd",
|
| 29 |
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"099c0519-640b-4eb7-867c-998dc337579d",
|
| 30 |
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"09b62a15-dd8b-4f47-881f-d3f1e5ea5ff4",
|
| 31 |
+
"09cd7ab9-f8cd-4bd4-bcd6-64f57eedea46",
|
| 32 |
+
"0a0184b3-9e1a-4c36-98f4-00ae3beb8f01",
|
| 33 |
+
"0a45a464-a909-4b7c-a3e0-cd6cfb4262e4",
|
| 34 |
+
"0a9800bb-b4a3-4162-b6a8-79a07c6131cc",
|
| 35 |
+
"0aafb6f1-6c10-4886-8f03-543988e02d9e",
|
| 36 |
+
"0b877123-0902-4432-b789-c4c6cc681df4",
|
| 37 |
+
"0b8ea3ec-e75b-41a1-9442-64f5fbc11a5a",
|
| 38 |
+
"0c15a331-09ac-445c-837f-6afb5e377e56",
|
| 39 |
+
"0c76b05a-ae67-4304-b42a-423b0b30016f",
|
| 40 |
+
"0ce74616-abf8-47c2-86d9-f821cd25efd3",
|
| 41 |
+
"0e8d3cb3-2e4c-47a0-8bb4-65d2cc3161c6",
|
| 42 |
+
"0eb65305-bb95-4bf1-a154-1b810c0cff25",
|
| 43 |
+
"0f306cdc-878a-4ea0-9e91-b97736731637",
|
| 44 |
+
"0f682180-4e80-46e9-86ac-8292bdf40400",
|
| 45 |
+
"0fcb5f66-4d56-4247-8413-aef51e484e67",
|
| 46 |
+
"0ffa052a-fe3f-46c2-999d-43bd56b7826e",
|
| 47 |
+
"107b7dce-aa70-4d32-b39a-bc97983148fd",
|
| 48 |
+
"10f59add-706e-41d0-87ed-9a55eb138a66",
|
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| 861 |
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}
|
unconditional_gmm_train_only.joblib
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b21e90b08314a4a0026339c3a1a7a6a812397beb415078dca434df1c4ae46836
|
| 3 |
+
size 9390
|