Datasets:

Axym-Labs
/

axobench-population

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.

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.