Datasets:

Axym-Labs
/

axobench

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
backend: string
coreneuron_gpu: bool
duration_ms: int64
elapsed_seconds: double
exc_gmax_scale: double
generator: struct<backend: string, biophysics_hoc_sha256: string, code_revision: struct<commit: string, dirty:  (... 456 chars omitted)
  child 0, backend: string
  child 1, biophysics_hoc_sha256: string
  child 2, code_revision: struct<commit: string, dirty: bool>
      child 0, commit: string
      child 1, dirty: bool
  child 3, data_version: string
  child 4, dt_ms: double
  child 5, exc_gmax_scale: double
  child 6, inh_gmax_scale: double
  child 7, input_routing: string
  child 8, input_sampler: string
  child 9, integrator: string
  child 10, mechanism_files: list<item: string>
      child 0, item: string
  child 11, mechanisms_sha256: string
  child 12, morphology_sha256: string
  child 13, neuron_version: string
  child 14, nseg_policy: string
  child 15, soma_samples_per_ms: int64
  child 16, spike_label_rule: string
  child 17, synapse_delay_ms: double
  child 18, synapses: string
  child 19, teacher: string
  child 20, template_hoc_sha256: string
  child 21, v_init_mv: double
inh_gmax_scale: double
input_event_policy: string
input_routing: struct<all_traces_identical: bool, defensibility_note: string, duplicated_source_sites_per_sign: str (... 446 chars omitted)
  child 0, all_traces_identical: bool
  child 1, defensibility_note: string
  child 2, duplicated_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 3, morphology_segments: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 4, n_traces: int64
  child 5, policy: string
  child 6, source_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 7, status: string
  child 8, unique_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 9, unused_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 10, used_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
intervention: null
intervention_dose: null
kind: string
morphology_id: string
morphology_path: string
samples: int64
shard_index: int64
teacher_balance_label: string
version: int64
shards: list<item: struct<count: int64, file: string, sample_ids: list<item: string>>>
  child 0, item: struct<count: int64, file: string, sample_ids: list<item: string>>
      child 0, count: int64
      child 1, file: string
      child 2, sample_ids: list<item: string>
          child 0, item: string
to
{'shards': List({'count': Value('int64'), 'file': Value('string'), 'sample_ids': List(Value('string'))}), 'version': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              backend: string
              coreneuron_gpu: bool
              duration_ms: int64
              elapsed_seconds: double
              exc_gmax_scale: double
              generator: struct<backend: string, biophysics_hoc_sha256: string, code_revision: struct<commit: string, dirty:  (... 456 chars omitted)
                child 0, backend: string
                child 1, biophysics_hoc_sha256: string
                child 2, code_revision: struct<commit: string, dirty: bool>
                    child 0, commit: string
                    child 1, dirty: bool
                child 3, data_version: string
                child 4, dt_ms: double
                child 5, exc_gmax_scale: double
                child 6, inh_gmax_scale: double
                child 7, input_routing: string
                child 8, input_sampler: string
                child 9, integrator: string
                child 10, mechanism_files: list<item: string>
                    child 0, item: string
                child 11, mechanisms_sha256: string
                child 12, morphology_sha256: string
                child 13, neuron_version: string
                child 14, nseg_policy: string
                child 15, soma_samples_per_ms: int64
                child 16, spike_label_rule: string
                child 17, synapse_delay_ms: double
                child 18, synapses: string
                child 19, teacher: string
                child 20, template_hoc_sha256: string
                child 21, v_init_mv: double
              inh_gmax_scale: double
              input_event_policy: string
              input_routing: struct<all_traces_identical: bool, defensibility_note: string, duplicated_source_sites_per_sign: str (... 446 chars omitted)
                child 0, all_traces_identical: bool
                child 1, defensibility_note: string
                child 2, duplicated_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 3, morphology_segments: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 4, n_traces: int64
                child 5, policy: string
                child 6, source_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 7, status: string
                child 8, unique_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 9, unused_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 10, used_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
              intervention: null
              intervention_dose: null
              kind: string
              morphology_id: string
              morphology_path: string
              samples: int64
              shard_index: int64
              teacher_balance_label: string
              version: int64
              shards: list<item: struct<count: int64, file: string, sample_ids: list<item: string>>>
                child 0, item: struct<count: int64, file: string, sample_ids: list<item: string>>
                    child 0, count: int64
                    child 1, file: string
                    child 2, sample_ids: list<item: string>
                        child 0, item: string
              to
              {'shards': List({'count': Value('int64'), 'file': Value('string'), 'sample_ids': List(Value('string'))}), 'version': Value('int64')}
              because column names don't match

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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 (tag `minimal-v2.0`)	2,000 train, 250 val, 250 test	14.6 GB
`full/`	released (tag `full-v2.0`)	8,000 train, 250 val, 250 test	35.6 GB

full/ contains every minimal/ shard unchanged, plus 6,000 more training traces per training cell. Its val/, test/ and interventions/ are the same files as in minimal/, so scores on the two variants are directly comparable. The extra shards were generated with the same simulator, gains and input statistics, and take shard indices from 20,000 up, so no index or seed of minimal/ is reused.

Download with the axobench package (axobench download-data --variant full, or axobench.hub.download_dataset), or fetch single folders with huggingface_hub.snapshot_download(..., allow_patterns=["full/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/` and `full/`

Folder	Content
`train/<cell>/`	`minimal/`: 6,000 shards, 24,000 traces (12 training cells x 2,000); `full/`: 24,000 shards, 96,000 traces (12 x 8,000)
`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_ and intervention_ prefixes, plus changed_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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