metadata

dataset_info:
  features:
    - name: spike_counts
      sequence:
        sequence: uint8
    - name: subject_id
      dtype: string
    - name: session_id
      dtype: string
    - name: segment_id
      dtype: string
    - name: source_dataset
      dtype: string
  splits:
    - name: train
      num_bytes: 33983349435.45733
      num_examples: 4141
    - name: test
      num_bytes: 344675362.5426727
      num_examples: 42
  download_size: 5954621801
  dataset_size: 34328024798
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
tags:
  - v1.0

The Neural Pile (primate)

This dataset contains 34.3 billion tokens of curated spiking neural activity data recorded from primates. The code and detailed instructions for creating this dataset from scratch can be found at this GitHub repository. The dataset takes up about 34 GB on disk when stored as memory-mapped .arrow files (which is the format used by the local caching system of the Hugging Face datasets library). The dataset comes with separate train and test splits. You can load, e.g., the train split of the dataset as follows:

ds = load_dataset("eminorhan/neural-pile-primate", num_proc=32, split='train')

and display the first data row:

>>> print(ds[0])
>>> {
'spike_counts': ...,
'subject_id': 'sub-Reggie',
'session_id': 'sub-Reggie_ses-20170115T125333_behavior+ecephys',
'segment_id': 'segment_2',
'source_dataset': 'even-chen'
}

where:

spike_counts is a uint8 array containing the spike count data. Its shape is (n,t) where n is the number of simultaneously recorded neurons in that session and t is the number of time bins (20 ms bins).
source_dataset is an identifier string indicating the source dataset from which that particular row of data came from.
subject_id is an identifier string indicating the subject the data were recorded from.
session_id is an identifier string indicating the recording session.
segment_id is a segment (or chunk) identifier useful for cases where the session was split into smaller chunks (we split long recording sessions (>10M tokens) into smaller equal-sized chunks of no more than 10M tokens), so the whole session can be reproduced from its chunks, if desired.

The dataset rows are pre-shuffled, so user do not have to re-shuffle them.