| --- |
| license: other |
| pretty_name: AxoBench Population |
| task_categories: |
| - time-series-forecasting |
| - tabular-regression |
| tags: |
| - neuroscience |
| - computational-neuroscience |
| - neuron-simulation |
| - population-modeling |
| - coreneuron |
| --- |
| |
| # AxoBench Population |
|
|
| AxoBench Population contains population-context neuron traces for training and |
| evaluating learned neuron surrogates under connected-network input |
| distributions. Connected AxoSim rollouts supply branch-resolved input |
| histories; role-correct biophysical teachers supply the target voltage and |
| event traces. |
|
|
| ## Size and splits |
|
|
| | Split | Contexts | Traces | NPZ shards | |
| |---|---:|---:|---:| |
| | `train` | 80 | 40,000 | 480 | |
| | `val` | 20 | 10,000 | 120 | |
| | total | 100 | 50,000 | 600 | |
|
|
| Contexts, rather than neighboring windows from one context, define the |
| train/validation boundary. The corpus has exact 80/20 |
| excitatory/inhibitory composition across six morphology conditions. |
|
|
| ## Shard schema |
|
|
| Each compressed NPZ shard contains: |
|
|
| - `inputs`: `int8[trace, time, 1278]`; |
| - `targets`: `float32[trace, time, 2]`; |
| - `soma_mv`: `float32[trace, time]`; |
| - `context_index`: `int16[trace]`; |
| - `target_is_inhibitory`: `bool[trace]`; and |
| - `target_role`: excitatory or inhibitory metadata. |
|
|
| The native timestep is 1 ms. `train/manifest.json` and `val/manifest.json` |
| record shard counts, sample IDs, schema, and SHA-256 hashes. |
|
|
| ## Validation boundary |
|
|
| All 600 shards passed the release validator for schema, finite values, sample |
| counts, role balance, manifest hashes, and disjoint context splits. This |
| establishes package integrity and population-compatible provenance; it does |
| not imply that one fixed training recipe will improve every connected |
| population metric. |
|
|
| ## Loading |
|
|
| The Hugging Face Dataset Viewer does not natively expand these high-dimensional |
| NPZ tensors. Download the required shards with `hf download` or |
| `huggingface_hub.snapshot_download`, then load a shard with `numpy.load`. |
|
|
| ```python |
| from pathlib import Path |
| |
| import numpy as np |
| |
| shard = next(Path("train").glob("*.npz")) |
| with np.load(shard, allow_pickle=False) as data: |
| inputs = data["inputs"] |
| targets = data["targets"] |
| ``` |
|
|
| ## Related datasets |
|
|
| - `Axym-Labs/axobench`: ordinary isolated-neuron benchmark traces. |
| - `Axym-Labs/axobench-interventions`: paired perturbation 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. |
|
|
