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int-brain-lab/
ea-decoder-channel-xgboost

Name: ea-decoder-channel-xgboost
Rating: 2.5 (7 reviews)

brain-region-classification

international-brain-lab

cc-by-4.0

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Ephys Atlas region classifier (2026_W32, Cosmos)

Predicts the brain region of each Neuropixels recording channel from electrophysiological features alone -- no histology required. Trained by the International Brain Laboratory on the Ephys Atlas feature release 2026_W32.

What you can and cannot do without IBL access. The model runs for anyone. Computing the input features from raw Neuropixels data needs ibllib / ibl-neuropixel, and the raw data itself is IBL-hosted. To try the model immediately, use the bundled sample under example/ -- no account, no raw data, no S3.

Quickstart

python

import pandas as pd
from ephysatlas import load_pretrained

model = load_pretrained("int-brain-lab/ea-decoder-channel-xgboost", revision="2026_W32")
df = pd.read_parquet("example/features_sample.parquet")   # or your own features
out = model.predict(df)
print(out[["predicted_acronym", "prediction_probability", "fold_agreement"]].head())

load_pretrained is the entry point for every ephysatlas model, whatever its family — it reads ephysatlas_model.json and returns the right wrapper. Use it rather than importing a concrete class, so your code keeps working as the package evolves.

predict returns one row per input channel, indexed identically to the input: predicted_acronym, its Allen predicted_atlas_id, the fold-averaged prediction_probability, a fold_agreement column (fraction of the 5 folds voting for the winner -- the natural uncertainty signal), and a p_<acronym> column per class.

The prediction columns are namespaced so that df.join(out) works: the feature table already carries histology-derived acronym / atlas_id columns, and predictions must not shadow them.

Inputs

50 features, listed in ephysatlas_model.json under inputs.features. Every one must be present; predict raises and names anything missing.
Indexed by (pid, channel), one row per recording channel.
Must be the denoised aggregated features of vintage 2026_W32 -- that is, the raw_ephys_features_denoised.pqt table produced by the Ephys Atlas aggregation pipeline, as loaded by ephysatlas.data.read_features_from_disk. Units are baked into that table by the pipeline (RMS features in dB, spike_count in log2), so feeding raw features, or features from a vintage whose units differ, produces confident nonsense. Run model.selftest() to confirm your install reproduces the shipped output before trusting it.

Performance

Pooled out-of-fold accuracy: 0.5962 over 13 Cosmos regions, 765 insertions. Splits are by insertion (pid), so no channel from a test insertion appears in training. See confusion_matrix.png.

Limitations

Trained on IBL Neuropixels 1.0 recordings in mouse. Transfer to NP2, other species or other rigs is untested.
Coverage follows IBL brain-wide-map targeting; rare regions are under-represented.
Cosmos is a coarse parcellation. Predictions are per-channel and spatially unregularised -- neighbouring channels can disagree.
Known-misaligned insertions were excluded from training.

Reproducibility

Pin the revision. revision="2026_W32" is an immutable tag. Omitting revision resolves to main, which tracks whichever model is currently recommended and will change when a new feature vintage is published — fine for a first look, not for anything you publish or re-run.

ephysatlas_model.json records the training-time environment (xgboost, scikit-learn, numpy, ephysatlas, python) and random_seed. Verify your install reproduces the shipped output:

model.selftest()

Note scikit-learn<1.9 is required (1.9 broke OneToOneFeatureMixin.get_feature_names_out, which the feature transformer relies on).

Citation

Please cite the International Brain Laboratory Ephys Atlas. Model id 2026_W32_Cosmos_guiltless-orange-mallard, feature vintage 2026_W32.