neuralset.extractors.neuro.SpikesExtractor

class neuralset.extractors.neuro.SpikesExtractor(*, event_types: Literal['Spikes'] = 'Spikes', aggregation: Literal['single', 'sum', 'mean', 'first', 'middle', 'last', 'cat', 'stack', 'trigger'] = 'single', allow_missing: bool = False, frequency: Literal['native'] | float = 'native', offset: float = 0.0, baseline: tuple[float, float] | None = None, scaler: None | Literal['RobustScaler', 'StandardScaler'] = None, scale_factor: float | None = None, clamp: float | None = None, channel_order: Literal['unique', 'original'] = 'unique', infra: MapInfra = MapInfra(folder=None, cluster=None, logs='{folder}/logs/{user}/%j', job_name=None, timeout_min=120, nodes=1, tasks_per_node=1, cpus_per_task=10, gpus_per_node=None, mem_gb=None, max_pickle_size_gb=None, slurm_constraint=None, slurm_partition=None, slurm_account=None, slurm_qos=None, slurm_use_srun=False, slurm_additional_parameters=None, conda_env=None, workdir=None, permissions=511, version='1', keep_in_ram=True, max_jobs=128, min_samples_per_job=1, forbid_single_item_computation=False, mode='cached'))[source][source]

Feature extractor for spike data stored in HDF5/NWB files.

Reads spike times from HDF5 files and creates a dense binned array of shape (n_units, n_time_bins) at the specified frequency.

The preprocessing steps, if specified, are ordered as follows: 1. Spike binning at target frequency 2. Scaling 3. Baseline correction (applied on segments) 4. Clamp (applied on segments)

Parameters:

  • frequency ("native" or float, default="native") – Target sampling frequency for spike binning. If "native", uses the frequency declared in the Spikes event.

  • offset (float, default=0.0) – Time offset (in seconds) applied to the segment window.

  • baseline (tuple of float, optional) – If provided as (start, end), defines the baseline correction window in seconds relative to the segment start.

  • scaler ({"RobustScaler", "StandardScaler"}, optional) – Scaling strategy to normalize channel data using scikit-learn scalers.

  • scale_factor (float, optional) – Multiplicative factor applied to the data after scaling but before clamping.

  • clamp (float, optional) – Maximum absolute value for clamping after preprocessing.

  • channel_order ({"unique", "original"}, default="unique") – "unique": channels are numbered based on unique names across all recordings. "original": channel indices follow per-recording order, enabling a fixed-size channel dimension across subjects.