CRCNS AC1 Dataset (Wehr + Asari subsets)

Dataset Source: AC1 Dataset

Citation:

Asari, H; Wehr, M; Machens, C; Zador, A. (2009).
Auditory cortex and thalamic neuronal responses to various natural and synthetic sounds.
CRCNS.org. http://dx.doi.org/10.6080/K0KW5CXR

Papers Using the Dataset:

Machens, C. K., Wehr, M. S., & Zador, A. M. (2004). “Linearity of Cortical Receptive Fields Measured with Natural Sounds.” J. Neurosci. 24(5):1089-1100. (the Wehr subset)
Asari, H. & Zador, A. M. (2009). “Long-Lasting Context Dependence Constrains Neural Encoding Models in Rodent Auditory Cortex.” J. Neurophysiol. 102(5):2638-2656. (the Asari subset)
Rançon, U., Masquelier, T., & Cottereau, B. R. (2025). “Temporal recurrence as a general mechanism to explain neural responses in the auditory system.” Commun. Biol. 8:1456.

What you get

A single :class:~deepSTRF.datasets.audio.CRCNSAC1Dataset exposes both historical subsets through the modern deepSTRF data paradigm ((B, N, R, T) outputs, NaN sentinels for sparse coverage). The experimenter argument filters between the two subsets; sites filters Asari’s A1 vs MGB recordings (Wehr is all A1).

See the inspect_crcns_ac1 notebook for a guided tour: subthreshold Vm vs spike counts, trial-to-trial variability, Wehr fragments vs Asari sequences, how the recording mode sets the signal type, and the under-the-hood MedGauss detrend + artifact gating.

from deepSTRF.datasets.audio import CRCNSAC1Dataset

ds = CRCNSAC1Dataset(
    path="~/Documents/NRFdatasets/Audio/CRCNS_AC1",
    experimenter=("wehr", "asari"),   # default both; tuple or single str
    sites=("A1", "MGB"),              # default both
    dt_ms=5.0,                        # matches Rançon 2025 Fig 2 settings
    download=False,                   # True needs $CRCNS_USERNAME / $CRCNS_PASSWORD
)

Signal type is set by the recording mode (not a user choice)

The response signal is determined by how each cell was recorded, because the recording mode dictates what signal physically exists:

recording mode	subset	`nrn_meta['signal_type']`	sign	pair with
whole-cell	Wehr, Asari A1	`'subthresh'` (MedGauss-detrended Vm)	signed	`mse_loss`
cell-attached	Asari MGB	`'spikes'` (Hann-smoothed rate)	≥ 0	`poisson_loss`

Cell-attached recordings have no intracellular Vm; whole-cell recordings have no spikes (blocked in Wehr, not analysed in Asari A1 per the paper). The loader derives the type per cell from recording_type, stores it in nrn_meta['signal_type'], and exposes the cohort-level ds.signal_type ('subthresh', 'spikes', or 'mixed'). Loading A1 + MGB together yields a 'mixed' cohort (signed-mV and spike-rate neurons side by side) and emits a warning — filter by site / signal_type before training one model across both.

Subset summary

	Recording	sf	Subjects	Stimuli	N (loader)
Wehr	whole-cell A1	4 kHz	anaesth. rat	15-s natural-sound fragments	25
Asari A1	whole-cell A1	10 kHz	anaesth. rat	spliced sequences of segments	39
Asari MGB	cell-attached MGB	10 kHz	anaesth. rat	same as Asari A1 (subset)	14

(The “N (loader)” numbers reproduce the Asari 2009 paper counts — 39 A1 + 14 MGB cells tested with natural-sound ensembles, plus the 25 Wehr sessions used by Rançon 2024/2025.)

Wehr stimuli are documented by their fragment name (humpback whale, jaguar mating call, etc.) and live under Stimuli/{fragments,category{1,2,3}}/<idx>.mat. Asari stimuli are sequences of 5 segments spliced together (e.g. 'Sequence 1: 2 1 3 1 4' indexes into the recording’s own param.stimulus[i].file lookup table — typically class/.mat plus suffixed amplitude / frequency variants). The loader resolves the exact segment files and records them in stim_meta['segment_files'].

Processing pipeline

The loader is fully Python (no MATLAB runtime). Each instantiation:

Auto-extracts the three CRCNS-AC1 zips if not already done (crcns-ac1.zip, crcns-ac1-asari-results-{1,2}.zip). Pass download=True (and CRCNS credentials) to fetch them from the NERSC mirror first.
Walks Results/ directories; one cell per session.
Detrends each Vm repeat with a MedGauss subtraction (median window 100 ms, gaussian σ 10 ms) — same family as the Rançon 2025 supplement’s “Detrending with MedGauss filter” recipe.
Gates repeats against three artifact tests (:class:~deepSTRF.datasets.audio._crcns_ac1_native.RepeatGating): amplitude clipping (|Vm| > abs_mv_max), sustained baseline steps (movement / dropout artifacts the MedGauss baseline can’t track), and cross-trial Pearson disagreement. Bad repeats drop; the rejection counts are exposed at ds._rejection_counter for diagnostics.
Spectrograms each unique stimulus once via a Goertzel STFT at log-spaced frequencies, directly at the target temporal resolution (hop = round(dt_ms × sf_stim / 1000)) — no two-step compute-then-downsample as the legacy MATLAB pipeline did.

For the cell-attached MGB cohort, the subthreshold step is replaced by spike extraction: drift-detrend → high-pass (subtract a 10 ms median, the Asari 2009 value) → threshold at 2.5 σ → 21 ms Hann smooth. On a sample MGB cell this yields ~12 Hz evoked rate, matching the paper’s 11.4 ± 16.9 Hz.

Reproducibility constants

For Rançon 2024/2025 numbers, the loader exports two constants:

WEHR_VALID_NEURONS — the 21 of 25 cells used in those papers (drops the unresponsive 1, 2, 4, 8 per Machens et al. 2004).
WEHR_NEURONS_SPLIT_NATURAL — per-neuron (train, val, test) stim counts used in those papers (indexed by _wehr_cell_idx in nrn_meta).

from deepSTRF.datasets.audio import (
    CRCNSAC1Dataset, WEHR_VALID_NEURONS, WEHR_NEURONS_SPLIT_NATURAL,
)
ds = CRCNSAC1Dataset(experimenter="wehr", dt_ms=5.0)
ds.select_pop_by_nrn_predicate(
    lambda n: n["_wehr_cell_idx"] in WEHR_VALID_NEURONS
)

Per-cell single-unit fits over this cohort reproduce the published Wehr numbers: StateNet-GRU ≈ 0.31 and Linear STRF ≈ 0.21 mean test cc_norm (Rançon et al. 2025, Table 1). For the Linear model use temporal_window_size ≈ 20–40 at dt_ms=5.0 (a 100–200 ms window) — a stateless STRF needs the long window to reach that score, whereas the GRU gets its temporal context from recurrence (T=1 input).

Spectrogram defaults

Both subsets share the same Goertzel STFT layout: 53 log-spaced bands from 100 Hz to 45 kHz at 6 bins/octave (the Asari 2025 setting). Wehr stimuli (content ≤ 22 kHz) get the same F; the top ~5 bands sit near the noise floor for them and are harmless. To recover the Wehr 2024 49-band setting exactly, pass fmax=25600.0 to the constructor.

Caveats

CRCNS account required for download=True (free registration at https://crcns.org/register; credentials can be provided via $CRCNS_USERNAME / $CRCNS_PASSWORD).
Vm drift + motion artifacts are common in these recordings; several repeats per stimulus get gated out by the artifact tests. Inspect ds._rejection_counter to see the breakdown.
Numerical fidelity vs Rançon 2024 / 2025. The Goertzel spectrogram is bit-equivalent (~fp64) to MATLAB’s logspectrogram, but the response cleanup pipeline differs in detail (we run artifact gating that the MATLAB pipeline did not). Expect model cc_norm within seed-level noise of the published numbers.
MGB spike window vs the prior pipeline. The legacy asari.py high-passed with median_filter(resp, 10) — 10 samples = 1 ms at 10 kHz, a units bug (its own comment cited “10 ms (cf. Asari et al.)”) that erased the ~1 ms spikes and left the threshold chasing noise. We use the paper-intended 10 ms, which recovers the published MGB firing rate. So MGB spike-rate numbers here will differ from a pipeline that reproduced the 1 ms bug.
Tone tuning curves and synthetic stimuli are not loaded in v1. Only naturalsound (Wehr) / naturalsound sequences (Asari) are ingested. Adding tones is a future extension; gated by a stimuli= kwarg.