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:
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.