nwb.ecephys.yaml

groups:
- neurodata_type_def: ElectricalSeries
  neurodata_type_inc: TimeSeries
  doc: A time series of acquired voltage data from extracellular recordings.
    The data field is an int or float array storing data in volts. The first
    dimension should always represent time. The second dimension, if present,
    should represent channels.
  attributes:
  - name: filtering
    dtype: text
    doc: Filtering applied to all channels of the data. For example, if this ElectricalSeries represents
      high-pass-filtered data (also known as AP Band), then this value could be "High-pass 4-pole Bessel filter
      at 500 Hz". If this ElectricalSeries represents low-pass-filtered LFP data and the type of filter is unknown,
      then this value could be "Low-pass filter at 300 Hz". If a non-standard filter type is used, provide as much
      detail about the filter properties as possible.
    required: false
  datasets:
  - name: data
    dtype: numeric
    dims:
    - - num_times
    - - num_times
      - num_channels
    - - num_times
      - num_channels
      - num_samples
    shape:
    - - null
    - - null
      - null
    - - null
      - null
      - null
    doc: Recorded voltage data.
    attributes:
    - name: unit
      dtype: text
      value: volts
      doc: Base unit of measurement for working with the data. This value is fixed to
        'volts'. Actual stored values are not necessarily stored in these units. To
        access the data in these units, multiply 'data' by 'conversion', followed by
        'channel_conversion' (if present), and then add 'offset'.
  - name: electrodes
    neurodata_type_inc: DynamicTableRegion
    doc: DynamicTableRegion pointer to the electrodes that this time series was generated from.
  - name: channel_conversion
    dtype: float32
    dims:
    - num_channels
    shape:
    - null
    doc: Channel-specific conversion factor. Multiply the data in the 'data' dataset by these
      values along the channel axis (as indicated by axis attribute) AND by the global
      conversion factor in the 'conversion' attribute of 'data' to get the data values in
      Volts, i.e, data in Volts = data * data.conversion * channel_conversion. This
      approach allows for both global and per-channel data conversion factors needed
      to support the storage of electrical recordings as native values generated by data
      acquisition systems. If this dataset is not present, then there is no channel-specific
      conversion factor, i.e. it is 1 for all channels.
    quantity: '?'
    attributes:
    - name: axis
      dtype: int32
      value: 1
      doc: The zero-indexed axis of the 'data' dataset that the channel-specific conversion
        factor corresponds to. This value is fixed to 1.

- neurodata_type_def: SpikeEventSeries
  neurodata_type_inc: ElectricalSeries
  doc: "Stores snapshots/snippets of recorded spike events (i.e., threshold crossings). This
    may also be raw data, as reported by ephys hardware. If so, the TimeSeries::description
    field should describe how events were detected. All SpikeEventSeries should
    reside in a module (under EventWaveform interface) even if the spikes were reported
    and stored by hardware. All events span the same recording channels and store
    snapshots of equal duration. TimeSeries::data array structure: [num events]
    [num channels] [num samples] (or [num events] [num samples] for single electrode)."
  datasets:
  - name: data
    dtype: numeric
    dims:
    - - num_events
      - num_samples
    - - num_events
      - num_channels
      - num_samples
    shape:
    - - null
      - null
    - - null
      - null
      - null
    doc: Spike waveforms.
    attributes:
    - name: unit
      dtype: text
      value: volts
      doc: Unit of measurement for waveforms, which is fixed to 'volts'.
  - name: timestamps
    dtype: float64
    dims:
    - num_times
    shape:
    - null
    doc: Timestamps for samples stored in data, in seconds, relative to the
      common experiment master-clock stored in NWBFile.timestamps_reference_time.
      Timestamps are required for the events. Unlike for TimeSeries, timestamps are
      required for SpikeEventSeries and are thus re-specified here.
    attributes:
    - name: interval
      dtype: int32
      value: 1
      doc: Value is '1'
    - name: unit
      dtype: text
      value: seconds
      doc: Unit of measurement for timestamps, which is fixed to 'seconds'.

- neurodata_type_def: FeatureExtraction
  neurodata_type_inc: NWBDataInterface
  default_name: FeatureExtraction
  doc: Features, such as PC1 and PC2, that are extracted from signals stored in a
    SpikeEventSeries or other source.
  datasets:
  - name: description
    dtype: text
    dims:
    - num_features
    shape:
    - null
    doc: Description of features (eg, ''PC1'') for each of the extracted features.
  - name: features
    dtype: float32
    dims:
    - num_events
    - num_channels
    - num_features
    shape:
    - null
    - null
    - null
    doc: Multi-dimensional array of features extracted from each event.
  - name: times
    dtype: float64
    dims:
    - num_events
    shape:
    - null
    doc: Times of events that features correspond to (can be a link).
  - name: electrodes
    neurodata_type_inc: DynamicTableRegion
    doc: DynamicTableRegion pointer to the electrodes that this time series was generated from.

- neurodata_type_def: EventDetection
  neurodata_type_inc: NWBDataInterface
  default_name: EventDetection
  doc: Detected spike events from voltage trace(s).
  datasets:
  - name: detection_method
    dtype: text
    doc: Description of how events were detected, such as voltage threshold, or dV/dT
      threshold, as well as relevant values.
  - name: source_idx
    dtype: int32
    dims:
    - num_events
    shape:
    - null
    doc: Indices (zero-based) into source ElectricalSeries::data array corresponding
      to time of event. ''description'' should define what is meant by time of
      event (e.g., .25 ms before action potential peak, zero-crossing time, etc).
      The index points to each event from the raw data.
  - name: times
    dtype: float64
    dims:
    - num_events
    shape:
    - null
    doc: Timestamps of events, in seconds.
    attributes:
    - name: unit
      dtype: text
      value: seconds
      doc: Unit of measurement for event times, which is fixed to 'seconds'.
  links:
  - name: source_electricalseries
    target_type: ElectricalSeries
    doc: Link to the ElectricalSeries that this data was calculated from. Metadata
      about electrodes and their position can be read from that ElectricalSeries so
      it's not necessary to include that information here.

- neurodata_type_def: EventWaveform
  neurodata_type_inc: NWBDataInterface
  default_name: EventWaveform
  doc: Represents either the waveforms of detected events, as extracted from a raw
    data trace in /acquisition, or the event waveforms that were stored during experiment
    acquisition.
  groups:
  - neurodata_type_inc: SpikeEventSeries
    doc: SpikeEventSeries object(s) containing detected spike event waveforms.
    quantity: '*'

- neurodata_type_def: FilteredEphys
  neurodata_type_inc: NWBDataInterface
  default_name: FilteredEphys
  doc: Electrophysiology data from one or more channels that has been subjected to filtering.
    Examples of filtered data include Theta and Gamma (LFP has its own interface).
    FilteredEphys modules publish an ElectricalSeries for each filtered channel or
    set of channels. The name of each ElectricalSeries is arbitrary but should be
    informative. The source of the filtered data, whether this is from analysis of
    another time series or as acquired by hardware, should be noted in each's TimeSeries::description
    field. There is no assumed 1::1 correspondence between filtered ephys signals
    and electrodes, as a single signal can apply to many nearby electrodes, and one
    electrode may have different filtered (e.g., theta and/or gamma) signals represented.
    Filter properties should be noted in the ElectricalSeries 'filtering' attribute.
  groups:
  - neurodata_type_inc: ElectricalSeries
    doc: ElectricalSeries object(s) containing filtered electrophysiology data.
    quantity: '+'

- neurodata_type_def: LFP
  neurodata_type_inc: NWBDataInterface
  default_name: LFP
  doc: LFP data from one or more channels. The electrode map in each published ElectricalSeries
    will identify which channels are providing LFP data. Filter properties should
    be noted in the ElectricalSeries 'filtering' attribute.
  groups:
  - neurodata_type_inc: ElectricalSeries
    doc: ElectricalSeries object(s) containing LFP data for one or more channels.
    quantity: '+'

- neurodata_type_def: ElectrodeGroup
  neurodata_type_inc: NWBContainer
  doc: A physical grouping of electrodes, e.g. a shank of an array.
  attributes:
  - name: description
    dtype: text
    doc: Description of this electrode group.
  - name: location
    dtype: text
    doc: Location of electrode group. Specify the area, layer, comments on estimation
      of area/layer, etc. Use standard atlas names for anatomical regions when possible.
  datasets:
  - name: position
    dtype:
    - name: x
      dtype: float32
      doc: x coordinate
    - name: y
      dtype: float32
      doc: y coordinate
    - name: z
      dtype: float32
      doc: z coordinate
    doc: stereotaxic or common framework coordinates
    quantity: '?'
  links:
  - name: device
    target_type: Device
    doc: Link to the device that was used to record from this electrode group.

# The types below have been deprecated
- neurodata_type_def: ClusterWaveforms
  neurodata_type_inc: NWBDataInterface
  default_name: ClusterWaveforms
  doc: DEPRECATED The mean waveform shape, including standard deviation, of the different
    clusters. Ideally, the waveform analysis should be performed on data that is only
    high-pass filtered. This is a separate module because it is expected to require
    updating. For example, IMEC probes may require different storage requirements
    to store/display mean waveforms, requiring a new interface or an extension of
    this one.
  datasets:
  - name: waveform_filtering
    dtype: text
    doc: Filtering applied to data before generating mean/sd
  - name: waveform_mean
    dtype: float32
    dims:
    - num_clusters
    - num_samples
    shape:
    - null
    - null
    doc: The mean waveform for each cluster, using the same indices for each wave
      as cluster numbers in the associated Clustering module (i.e, cluster 3 is in
      array slot [3]). Waveforms corresponding to gaps in cluster sequence should
      be empty (e.g., zero- filled)
  - name: waveform_sd
    dtype: float32
    dims:
    - num_clusters
    - num_samples
    shape:
    - null
    - null
    doc: Stdev of waveforms for each cluster, using the same indices as in mean
  links:
  - name: clustering_interface
    target_type: Clustering
    doc: Link to Clustering interface that was the source of the clustered data

- neurodata_type_def: Clustering
  neurodata_type_inc: NWBDataInterface
  default_name: Clustering
  doc: DEPRECATED Clustered spike data, whether from automatic clustering tools (e.g.,
    klustakwik) or as a result of manual sorting.
  datasets:
  - name: description
    dtype: text
    doc: Description of clusters or clustering, (e.g. cluster 0 is noise, clusters
      curated using Klusters, etc)
  - name: num
    dtype: int32
    dims:
    - num_events
    shape:
    - null
    doc: Cluster number of each event
  - name: peak_over_rms
    dtype: float32
    dims:
    - num_clusters
    shape:
    - null
    doc: Maximum ratio of waveform peak to RMS on any channel in the cluster (provides
      a basic clustering metric).
  - name: times
    dtype: float64
    dims:
    - num_events
    shape:
    - null
    doc: Times of clustered events, in seconds. This may be a link to times field
      in associated FeatureExtraction module.