---
license: cc-by-4.0
library_name: pytorch
tags:
  - neuroscience
  - electrophysiology
  - neuropixels
  - unit-level-encoder
  - international-brain-lab
---

# Ephys Atlas unit-level interpolation model (2026_W42)

Predicts **which kinds of neurons you expect to record at a brain position**, and their expected
waveform phenotype. Trained by the
[International Brain Laboratory](https://www.internationalbrainlab.com/) on spike-sorted units of
the Ephys Atlas release `2026_W41` (56497 training units).

The model has four stages:

1. a multimodal autoencoder embeds each unit's multi-channel **waveform**, 3D
   **autocorrelogram** and **population coupling** (waveform, acg, stpc) into a
   60-d latent -- its phenotype;
2. a **hierarchical Gaussian mixture** over the standardised latents: 30 putative cell
   types, each split into at most 10 sub-types (300 in all). Assignment is
   top-down -- the most likely type, then the most likely of its sub-types -- so a unit's
   sub-type always lies inside its type. The types and sub-types are global;
3. the **sub-type weights depend on the molecular and cellular context** (PCA volumes of the
   molecular and cellular atlases) at the position: one network predicts the type weights, a
   second the shares of the sub-types within each type;
4. a **members readout** maps the model back to 10 interpretable waveform features:
   `depolarisation_slope`, `recovery_slope`, `repolarisation_slope`, `spatial_spread_um`, `tip_val`, `spike_width_secs`, `predepolarisation_width_secs`, `spike_amplitude`, `peak_to_trough_ratio_log`, `polarity`. Each sub-type is represented by its training units, from anywhere in the brain,
   weighted by their posterior of the sub-type.

> **The input is a position.** `predict` takes `x, y, z` (IBL/Allen frame, metres) and returns the
> expected phenotype there: the local sub-type weights times each sub-type's expected features.
> `mixture_weights` returns the local putative cell-type composition itself.

## Quickstart

```python
import pandas as pd
from ephysatlas import load_pretrained

model = load_pretrained("int-brain-lab/ea-encoder-unit", revision="2026_W42")
df = pd.read_parquet("example/positions_sample.parquet")   # positions (x, y, z)
out = model.predict(df)                                     # one pred_<feature> column each
types = model.mixture_weights(df, level="type")             # putative cell-type composition
subtypes = model.mixture_weights(df)                        # ... and of the sub-types
draws = model.sample(df, n_samples=16)                      # training units drawn at each position
model.selftest()                                            # reproduces the shipped outputs
```

Units you recorded yourself can be embedded with `model.encode(waveform, acg, stpc)` (arrays shaped
as in `config.json`), then assigned to a putative cell type and sub-type with `model.assign(z)`.

## What ships here

- `autoencoder.pt`, `shared_latent_scaler.joblib` -- the phenotype encoder and its latent scaler.
- `types_gmm.joblib`, `subtypes_gmm.joblib`, `subtype_parent.npy` -- the types, the sub-types and
  the type of each sub-type.
- `context_transform.joblib`, `context_weights_types.pt`, `context_weights_subtypes.pt` -- context
  -> type and sub-type weights.
- `members.npz` -- the phenotype features of the training units and their sub-type posteriors
  (the readout; part of the model, like the stored points of any nearest-neighbour model).
- `agea_vol_pca.npy`, `merfish_vol_pca.npy` -- the molecular and cellular context volumes, the same frozen
  volumes as the channel-level model `int-brain-lab/ea-encoder-channel` of release `2026_W41`.
- `split.json`, `config.json`, `preprocessing/unit_stats.npz`, `results/` -- the probe split, the
  training configuration, the training-data statistics and held-out evaluation summaries.

The recorded per-unit dataset (waveforms, ACGs, positions) is **not** here: it stays on IBL S3 and
is only needed to retrain the model or to reproduce the atlas-wide analyses.

> **First run downloads the Allen volume.** Sampling the context constructs an `AllenAtlas`, which
> fetches the Allen CCF volume (public, no account, a few hundred MB) once per machine.

## Reproducibility

**Pin the revision.** `revision="2026_W42"` is an immutable tag; omitting it resolves to `main`,
which tracks the currently recommended model. `ephysatlas_model.json` records the training-time
environment and random seed; `model.selftest()` verifies your install reproduces the shipped
output (no IBL account needed).

## Citation

Please cite the International Brain Laboratory Ephys Atlas. Model id `2026_W42_unit`, vintage
`2026_W42`.