The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AxoBench v2
AxoBench is a benchmark for data-driven surrogates of layer-5 pyramidal neurons: given six seconds of synaptic input events, predict the somatic voltage and spikes of a detailed NEURON model of the cell. Version 2 covers 15 cells, adds paired interventions at three doses each, and stores the somatic voltage at 4 kHz in addition to the 1 ms targets.
Variants
Each variant is a self-contained dataset root in its own top-level folder.
| Folder | Status | Traces per training cell | Size |
|---|---|---|---|
minimal/ |
released | 2,000 train, 250 val, 250 test | 14.6 GB |
Larger variants will be added next to minimal/ with the same layout.
Download with the axobench package (axobench download-data, or
axobench.hub.download_dataset), or fetch single folders with
huggingface_hub.snapshot_download(..., allow_patterns=["minimal/val/*"]).
Cells
- 14 Allen Cell Types all-active models of mouse layer-5 cells with intact apical dendrites (Gouwens et al. 2018), each with its fitted biophysics and a per-cell excitatory gain calibrated to a common firing rate.
- The NeuronIO layer-5b cell (Hay et al. 2011,
neuronio_hay_cell1) as the anchor to earlier work.
Three Allen cells (354190013, 480353286, 488683425) are held out: they appear
only in test (zero-shot track). morphologies.json lists every cell with
its source and held-out flag; morphologies/ holds the Allen SWC files.
Layout of minimal/
| Folder | Content |
|---|---|
train/<cell>/ |
6,000 shards, 24,000 traces (12 training cells x 2,000) |
val/<cell>/ |
756 shards, 3,000 traces (12 x 250) |
test/<cell>/ |
945 shards, 3,750 traces (15 x 250) |
interventions/<condition>_<dose>/ |
15 folders, 600 pairs each (40 per cell), 9,000 pairs in total |
morphologies/, morphologies.json |
cell index and reconstructions |
dataset_package_manifest.json |
counts per split, cell and condition; generator settings and gains |
checksums.sha256 |
SHA-256 of every file in the variant |
Interventions: event dropout, excitatory dropout and inhibitory dropout at 10, 35 and 70%; site silencing at 10, 25 and 50%; temporal jitter of 1, 2 and 8 ms.
Shard schema
Each .npz shard has a JSON sidecar with the generator settings. Each split
and intervention folder has a manifest.json that lists its shards (paths
relative to the folder; split shards sit in one subfolder per cell) and sample
ids.
inputs:(n, 6000, 1278)int8, input events per 1 ms bin on 639 excitatory and 639 inhibitory synapse sites.targets:(n, 6000, 2)float32 at 1 ms: spike indicator (0/1) and the NeuronIO-style somatic target, (min(V, -55 mV) + 67.7 mV) / 10.soma_mv:(n, 6000)raw somatic voltage (mV) at 1 ms.soma_mv_hr:(n, 24000)raw somatic voltage (mV) at 4 kHz.- Intervention shards hold the same arrays with
baseline_andintervention_prefixes, pluschanged_event_mask.
Withheld data
A confirmation split (private-test, 125 traces per cell) is not published.
Predictions on it are scored on request, rate-limited.
License and attribution
The data are released under CC BY-NC 4.0. The Allen cells are simulated on reconstructions and models from the Allen Cell Types Database, whose Terms of Use allow derived works for research and other noncommercial purposes only. Commercial use needs written permission from the Allen Institute.
If you use these data, please cite:
- Allen Institute for Brain Science (2015). Allen Cell Types Database [dataset]. Available from celltypes.brain-map.org.
- Gouwens, N. W., et al. (2018). Systematic generation of biophysically detailed models for diverse cortical neuron types. Nature Communications, 9, 710. https://doi.org/10.1038/s41467-017-02718-3
- Hay, E., Hill, S., Schürmann, F., Markram, H., & Segev, I. (2011). Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Computational Biology, 7(7), e1002107. https://doi.org/10.1371/journal.pcbi.1002107
- Beniaguev, D., Segev, I., & London, M. (2021). Single cortical neurons as deep artificial neural networks. Neuron, 109(17), 2727–2739.e3. https://doi.org/10.1016/j.neuron.2021.07.002 (input statistics and generator design)
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