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license: other
pretty_name: AxoBench Interventions
task_categories:
- time-series-forecasting
- tabular-regression
tags:
- neuroscience
- computational-neuroscience
- neuron-simulation
- interventions
- robustness
- coreneuron
---
# AxoBench Interventions
AxoBench Interventions is the paired perturbation part of AxoBench, released
separately so intervention studies do not need to download the ordinary
single-neuron benchmark corpus. Every example contains matched baseline and
intervention inputs and teacher outputs.
## Conditions
| Condition | Paired traces | NPZ shards |
|---|---:|---:|
| `event_dropout` | 2,500 | 40 |
| `exc_dropout` | 2,500 | 40 |
| `inh_dropout` | 2,500 | 40 |
| `site_silence` | 2,500 | 40 |
| `temporal_jitter` | 2,500 | 40 |
| total | 12,500 | 200 |
Each condition directory also contains 40 JSON sidecars and one manifest.
## Shard schema
Compressed NPZ shards contain paired arrays including
`baseline_inputs`, `intervention_inputs`, `baseline_targets`, and
`intervention_targets`. The condition manifests provide sample IDs and shard
counts; `checksums.sha256` covers the complete release package.
## Loading
The Hugging Face Dataset Viewer does not natively expand these high-dimensional
NPZ tensors. Download a condition or the full repository and load shards with
NumPy:
```python
from pathlib import Path
import numpy as np
shard = next(Path("event_dropout").glob("*.npz"))
with np.load(shard, allow_pickle=False) as data:
baseline = data["baseline_inputs"]
intervention = data["intervention_inputs"]
```
## Related datasets
- `Axym-Labs/axobench`: ordinary isolated-neuron benchmark traces.
- `Axym-Labs/axobench-population`: connected population-context traces.
The files are provided for research use. Users are responsible for checking
the terms of the upstream simulator, morphology, and teacher-model resources
used in their application.
|