---
license: cc-by-sa-4.0
pretty_name: SPE-1 (Marques-Smith et al. 2018) — Arrow conversion for spike localization benchmarks
language:
- en
tags:
- neuroscience
- electrophysiology
- spike-sorting
- spike-localization
- neuropixels
- patch-clamp
- ground-truth
size_categories:
- 100GB<n<1TB
task_categories:
- other
annotations_creators:
- expert-generated
source_datasets:
- extended|crcns-spe-1
configs:
- config_name: c5
  data_files:
  - {split: train, path: c5/train/data-*.arrow}
  - {split: test,  path: c5/test/data-*.arrow}
- config_name: c14
  data_files:
  - {split: train, path: c14/train/data-*.arrow}
  - {split: test,  path: c14/test/data-*.arrow}
- config_name: c15
  data_files:
  - {split: train, path: c15/train/data-*.arrow}
  - {split: test,  path: c15/test/data-*.arrow}
- config_name: c16
  data_files:
  - {split: train, path: c16/train/data-*.arrow}
  - {split: test,  path: c16/test/data-*.arrow}
- config_name: c19
  data_files:
  - {split: train, path: c19/train/data-*.arrow}
  - {split: test,  path: c19/test/data-*.arrow}
- config_name: c24
  data_files:
  - {split: train, path: c24/train/data-*.arrow}
  - {split: test,  path: c24/test/data-*.arrow}
- config_name: c26
  data_files:
  - {split: train, path: c26/train/data-*.arrow}
  - {split: test,  path: c26/test/data-*.arrow}
- config_name: c28
  data_files:
  - {split: train, path: c28/train/data-*.arrow}
  - {split: test,  path: c28/test/data-*.arrow}
- config_name: c29
  data_files:
  - {split: train, path: c29/train/data-*.arrow}
  - {split: test,  path: c29/test/data-*.arrow}
- config_name: c37
  data_files:
  - {split: train, path: c37/train/data-*.arrow}
  - {split: test,  path: c37/test/data-*.arrow}
- config_name: c45
  data_files:
  - {split: train, path: c45/train/data-*.arrow}
  - {split: test,  path: c45/test/data-*.arrow}
- config_name: c46
  data_files:
  - {split: train, path: c46/train/data-*.arrow}
  - {split: test,  path: c46/test/data-*.arrow}
---

# SPE-1 — Paired patch-clamp + Neuropixels recordings (Arrow conversion)

This dataset is an Arrow / 🤗 `datasets` re-packaging of the SPE-1 ground-truth electrophysiology dataset by [Marques-Smith et al. (2018)](https://github.com/kampff-lab/sc.io/tree/master/Paired%20Recordings), preprocessed and split into per-cell configurations for use with the spike-localization benchmarks by [Zhao et al. (2026)](https://github.com/haozhao1996/Spike-Localization-Algorithms).

The original raw recordings (~270 GB of int16 binaries) are not included here, but can be downloaded using the included scripts if needed.

## License

The original SPE-1 release is distributed under CC-BY-SA 4.0 (see `Licence CC BYSA 4.0.pdf`). This Arrow conversion inherits the same license.

Original sources:
- CRCNS: <https://crcns.org/data-sets/methods/spe-1>
- Google Drive mirror: <https://drive.google.com/drive/folders/13GCOuWN4QMW6vQmlNIolUrxPy-4Wv1BC>

## Citation

If you use this dataset, please cite the original recordings and the benchmark methodology:

- The original publication describing the SPE-1 dataset: Marques-Smith, A., Neto, J.P., Lopes, G., Nogueira, J., Calcaterra, L., Frazão, J., Kim, D., Phillips, M., Dimitriadis, G., Kampff, A.R. (2018). *Recording from the same neuron with high-density CMOS probes and patch-clamp: a ground-truth dataset and an experiment in collaboration.* bioRxiv 370080; doi: https://doi.org/10.1101/370080
- The SPE-1 dataset itself: André Marques-Smith, Joana P. Neto, Gonçalo Lopes, Joana Nogueira, Lorenza Calcaterra, João Frazão, Danbee Kim, Matthew G. Phillips, George Dimitriadis and Adam R. Kampff (2018); Simultaneous patch-clamp and dense CMOS probe extracellular recordings from the same cortical neuron in anaesthetized rats. CRCNS.org http://dx.doi.org/10.6080/K0J67F4T
- The spike-localization benchmark: Zhao, H., Zhang, X., et al. (2026). Benchmarking spike source localization algorithms in high density probes. https://doi.org/10.1371/journal.pcbi.1014059

## Cells included

12 cells from the SPE-1 release are converted:

| Cell | Notes                                                                       |
| ---- | --------------------------------------------------------------------------- |
| c5   | Longest WC-IC recording — used as the whole-cell V(t) reference             |
| c14, c15, c16, c19, c24, c26, c28, c29, c37, c45, c46 | The 11 juxtacellular cells selected by Zhao et al. 2026 |

For each cell, the first 10 s (settling period) are skipped and 300 s of recording are kept.

## Repository layout

```
frthjf/spe1-zhao2026-benchmark
├── README.md                       (this file)
├── chanMap.mat                     2-D probe geometry (384 × 2 µm, x and y)
├── Data Summary.xlsx               per-cell ground-truth electrode (chan_predicted)
├── Data Summary.csv                  (same converted to plain text csv)
├── Recording Catalogue.pdf         original SPE-1 catalogue
├── Licence CC BYSA 4.0.pdf         original SPE-1 license
├── localization_benchmark.json     COM / MT / GC template-level numbers
│                                   produced by scripts/reproduce_zhao2026.py
├── scripts/                        self-contained reproduction pipeline
│   ├── prepare.sh                    download (CRCNS) + run convert_to_arrow
│   ├── convert_to_arrow.py           SPE-1 raw → Arrow conversion
│   └── reproduce_zhao2026.py         COM / MT / GC baselines (Zhao et al. 2026)
└── c{ID}/                          one HF DatasetDict per cell
    ├── dataset_dict.json
    ├── conversion_metadata.json    (see schema below)
    ├── dataset_stats.json          per-channel median / IQR / abs-IQR
    ├── templates.npz               peak-aligned mean template + probe geometry
    ├── patch.npz                   ground-truth patch trace + spike times
    ├── patch_quicklook.png         sanity-check plot of patch.npz (where shipped)
    ├── train/
    │   ├── dataset_info.json
    │   ├── state.json
    │   └── data-*.arrow            ~ 138 MB per 3-s segment
    └── test/
        └── data-*.arrow
```

## Per-cell file schemas

### Arrow segments (`train/`, `test/`)

Each row = one ~3-second segment of preprocessed Neuropixels recording (bandpass 300-3000 Hz + global common-median reference, gain -> µV applied).
80 % of segments -> `train`, 20 % -> `test` (all segments are in order, so concatenating train+test yields original data).

| Column      | Type                  | Shape / units                              | Description                                          |
| ----------- | --------------------- | ------------------------------------------ | ---------------------------------------------------- |
| `sample_id` | `string`              | scalar                                     | Zero-padded NPX sample index of the segment start    |
| `cp`        | `list<list<float32>>` | `[384][T]` (T = ~90 000 samples ≈ 3 s)     | Per-channel preprocessed voltage traces (µV)         |
| `cit`       | `list<list<int32>>`   | `[384][n_spikes_on_channel]`               | Per-channel unit indices for each ground-truth spike (always `0` since single patched neuron) |
| `ctt`       | `list<list<float64>>` | `[384][n_spikes_on_channel]` (ms)          | Spike times within the segment, in milliseconds      |

Spike events are only assigned to the unit's peak channel (`unit_peak_channel`); the other 383 channels carry empty `cit`/`ctt` lists for that segment. Sampling frequency is 30 kHz.

### `templates.npz`

Mean spike template extracted with the Zhao et al. 2026 method (1 ms before + 2 ms after the patch-clamp peak; raw mean over snippets).

```python
import numpy as np
tpl = np.load("c14/templates.npz")
tpl["templates"]            # float32 [1, 90, 384]    (units x time x channels)
tpl["sampling_frequency"]   # 30000.0
tpl["probe_positions"]      # float32 [384, 2]        (x, y in um)
tpl["nbefore"], tpl["nafter"]  # 30, 60
```

### `patch.npz`

Authoritative patch-clamp recording for the same window used by the Arrow splits.

```python
patch = np.load("c5/patch.npz", allow_pickle=False)
patch["patch_v"]        # float32 [N]    patch-clamp voltage (mV)
patch["t_patch_s"]      # float64 [N]    timestamps (s, NPX-aligned)
patch["spike_times_s"]  # float64 [K]    GT spike times (s, NPX-aligned)
patch["patch_type"]     # 0-d str        "wc-ic" / "juxta" / etc.
patch["cell_id"]        # 0-d str        e.g. "c14"
```

### `conversion_metadata.json`

```json
{
  "source_metadata": {
    "mode": "spe1_paired_recording",
    "dataset": "Marques-Smith et al. (2018) \u2014 CRCNS spe-1",
    "cell_id": "c14",
    "skip_seconds": 10.0,
    "duration_s": 300.0,
    "patch_sample_rate_hz": 50023.0,
    "preprocessing": "bandpass_300_3000Hz + global_CMR",
    "spike_source": "wc_spike_samples.npy (authoritative GT)"
  },
  "template_metadata": {
    "extraction_method": "hao2026_raw_mean_template",
    "ms_before": 1.0,
    "ms_after": 2.0,
    "nbefore": 30,
    "nafter": 60,
    "n_spikes_total": 814,
    "n_spikes_used": 814,
    "n_out_of_bounds_discarded": 0
  },
  "sampling_frequency": 30000.0,
  "num_units": 1,
  "num_channels": 384,
  "unit_peak_channel": {
    "0": 161
  },
  "unit_locations_um": {
    "0": [
      27.0,
      1600.0,
      0.0
    ]
  },
  "gt_electrode_chan_idx": 159,
  "bad_channel_ids": [
    36,
    75,
    112,
    151,
    188,
    303
  ],
  "n_spikes_total": 814,
  "n_spikes_used_for_templates": 814,
  "segment_duration_s": 3.0,
  "train_fraction": 0.8
}
```

`unit_locations_um[0]` is the `(x, y, z=0)` µm position of the ground-truth electrode (`chan_predicted` from `Data Summary.xlsx`), and serves as the regression target for all localization metrics. Channel indices listed in `bad_channel_ids` were flagged by SpikeInterface's `detect_bad_channels` on the bandpass-filtered (pre-CMR) recording and should be masked before running amplitude-weighted localizers (COM / MT / GC).

## Quick start

### Load one cell

```python
from datasets import load_dataset

ds = load_dataset("path/to/spe1", name="c14")   # -> {'train', 'test'}
row = ds["train"][0]
print(row["sample_id"], len(row["cp"]), len(row["cp"][0]))  # 384 channels x ~90k samples
```

Or directly from disk after a clone / `huggingface-cli download`:

```python
from datasets import load_from_disk
dsd = load_from_disk("spe1/c14")
```

### Reproduce Hao et al. 2026 baselines (template-level localization)

The self-contained [`systems/datasets/spe1/scripts/reproduce_zhao2026.py`](systems/datasets/spe1/scripts/reproduce_zhao2026.py) runs Center-of-Mass, Monopolar-Triangulation and Grid-Convolution directly on the shipped `templates.npz` files:

```bash
python scripts/reproduce_zhao2026.py                    # all 11 Hao cells
python scripts/reproduce_zhao2026.py --cells c14 c45    # subset
python scripts/reproduce_zhao2026.py --with-spikes      # also spike-level Acc/MAE (slower)
```

Results are printed and written to `localization_benchmark.json`. Bad channels (`bad_channel_ids` in `conversion_metadata.json`) are masked automatically before localization, exactly as in the published baseline.

## Reproducing the Arrow conversion from scratch

Only needed if you want to re-derive the Arrow files from the ~270 GB raw binaries (e.g. to change `duration_s`, `segment_duration_s`, or the preprocessing pipeline). The dataset ships a self-contained pipeline in [`scripts/`](scripts) that depends only on standard PyPI packages (`requests`, `scipy`, `pandas`, `openpyxl`, `spikeinterface`, `datasets`, `numpy`):

```bash
# 1. Register a free CRCNS account at https://crcns.org/register
# 2. From the dataset root:
CRCNS_USERNAME=<user> CRCNS_PASSWORD=<pass> bash scripts/prepare.sh
```

`prepare.sh` will:
1. Download `chanMap.mat` and `Data Summary.xlsx` from CRCNS into `.raw/`
   (and mirror them at the dataset root so consumers don't need `.raw/`).
2. Download and extract `c{ID}.tar.gz` for each requested cell into
   `.raw/Recordings/c{ID}/`.
3. Run `scripts/convert_to_arrow.py` once per cell to produce the
   per-cell Arrow `train/`/`test/` splits + `templates.npz` +
   `conversion_metadata.json`.

Environment overrides (see `prepare.sh` header for full list):

| Variable        | Default                          | Description                       |
| --------------- | -------------------------------- | --------------------------------- |
| `SPE1_RAW_DIR`  | `./.raw`                         | Where raw downloads land          |
| `SPE1_OUT_DIR`  | `.` (dataset root)               | Where Arrow datasets are written  |
| `SPE1_CELLS`    | All 12 cells                     | Space-separated cell IDs          |
| `SPE1_DURATION` | `300`                            | Seconds of recording per cell     |
| `PYTHON`        | `python3`                        | Python interpreter                |

## Provenance & preprocessing summary

- Probe: Neuropixels 1.0, 384 channels, 30 kHz, int16 raw → µV (gain 2.34375 µV/bit), 2-D probe geometry from `chanMap.mat`.
- Filtering: causal bandpass 300–3000 Hz (Butterworth) followed by global common-median reference, applied via SpikeInterface (`spre.bandpass_filter` + `spre.common_reference`).
- Bad-channel detection: `spre.detect_bad_channels` on the bandpass output (before CMR) — channels listed in `bad_channel_ids` should be masked before running amplitude-weighted localizers.
- Ground-truth spikes: from `wc_spike_samples.npy` (authoritative GT shipped by SPE-1) when available, otherwise threshold detection on the patch trace (12 x MAD below median, 1 ms refractory).
- Templates: peak-aligned mean over all GT snippets, 1 ms before + 2 ms after peak, no per-spike realignment.
- Splits: contiguous 3-s segments, first 80 % → `train`, last 20 % → `test`.

## Total size

~145 GB across 12 cells (≈ 13 GB / cell, c37 is ~5 GB and c29 ~12 GB because of shorter usable recording windows).