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The dataset generation failed
Error code: DatasetGenerationError
Exception: AttributeError
Message: 'list' object has no attribute 'get'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1593, in _prepare_split_single
example = self.info.features.encode_example(record) if self.info.features is not None else record
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^
AttributeError: 'list' object has no attribute 'get'
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/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1633, in _download_and_prepare
super()._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1438, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1616, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
json dict | __key__ string | __url__ string |
|---|---|---|
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"n_synapses": null,
"n_total": 41,
"seed": null,
"session_id"... | allen_epochs/allen_721123822_running_2698 | hf://datasets/ronniebasak/ach-cortical-circuits@a40fd1cd6dc32ae477c04a1b04003372a345f42b/data/allen_epochs.tar.gz |
End of preview.
ACh Cortical Circuits: Cross-Cortical Transfer Dataset
15,000 simulated cortical microcircuits with acetylcholine (ACh) modulation + 2,678 real mouse V1 recording epochs from the Allen Brain Observatory.
Dataset Description
This dataset accompanies the paper "Cross-Cortical Transfer of Cholinergic Modulation Principles via Biophysical Simulation Surrogates" (CAISc 2026).
Simulated Circuits (15,000 total)
Three mechanistic variants of ACh modulation, each with 5,000 circuits × 11 ACh levels = 55,000 simulations per variant:
| Variant | ACh Mechanism | File |
|---|---|---|
| v14a | Synaptic suppression only (E2 dose-response) | v14a_circuits.tar.gz |
| v14b | Depolarization only (OU mean shift) | v14b_circuits.tar.gz |
| v14c | Both mechanisms combined | v14c_circuits.tar.gz |
Parameters source: Ramaswamy, Colangelo & Markram (2018), Frontiers in Neural Circuits — rat somatosensory cortex (S1).
Simulator: Jaxley (JAX-based, GPU-accelerated)
Each circuit JSON contains:
- Circuit parameters (n_neurons, connectivity, synaptic conductances, OU noise params)
- ACh level (0.0 to 1.0 in 0.1 steps)
- 11 population statistics (firing rate, CV ISI, Fano factor, synchrony index, pairwise correlation, peak frequency, spectral power, etc.)
Allen Brain Observatory Epochs (2,678 total)
Real mouse V1 (area VISp) neural recordings from the Allen Brain Observatory Visual Coding Neuropixels dataset:
- 2,325 running epochs + 353 stationary epochs
- 44 sessions, 16 with both behavioral states
- Quality filtered (presence_ratio > 0.95, ISI violations < 0.5)
- Same 11 population statistics as simulated data
File: allen_epochs.tar.gz
Model Checkpoints
12 trained surrogate models (3 architectures × 3 ACh variants + 3 baselines):
- CircuitTransformer (222K params)
- CircuitMLP (5.6K params)
- XGBoost
File: checkpoints.tar.gz
Key Results
| Statistic | Model S (syn) | Model D (depol) | Model SD (both) |
|---|---|---|---|
| Pairwise correlation | 75.0% | 25.0% | 25.0% |
| Mean firing rate | 12.5% | 87.5% | 87.5% |
| Spectral power | 68.8% | 68.8% | 68.8% |
Key finding: Synaptic suppression captures correlation structure; depolarization captures rate modulation. The mechanisms are dissociable in sim-to-real transfer.
Citation
@inproceedings{neuropeek2026crosstransfer,
title={Cross-Cortical Transfer of Cholinergic Modulation Principles via Biophysical Simulation Surrogates},
author={NeuroPeek AI and Basak, Sohan},
booktitle={1st Conference for AI Scientists (CAISc 2026)},
year={2026}
}
License
MIT
Acknowledgments
- Simulation parameters from Ramaswamy et al. 2018
- Real data from Allen Brain Observatory
- Simulation engine: Jaxley (Deistler et al., Nature Methods 2025)
- Compute: Modal cloud GPUs (~$230 total)
- Built with NeuroPeek AI Agents
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