From d46adae26d9c988e703cb43ee5e9700ac0105819 Mon Sep 17 00:00:00 2001 From: ehennestad Date: Mon, 25 Nov 2024 14:25:49 +0100 Subject: [PATCH 1/6] Add MotionCorrection, CorrectedImageStack and DeltaFOverF to ophys tutorial (#629) * Add MotionCorrection, CorrectedImageStack and DeltaFOverF to ophys tutorial * Fix, add original of CorrectedImageStack as link --- +tests/+util/getPythonPath.m | 2 +- tutorials/html/ophys.html | 176 ++++++++++++++++++++++---------- tutorials/ophys.mlx | Bin 172455 -> 173820 bytes tutorials/private/mcode/ophys.m | 114 +++++++++++++++++---- 4 files changed, 218 insertions(+), 74 deletions(-) diff --git a/+tests/+util/getPythonPath.m b/+tests/+util/getPythonPath.m index b7b3c494..304cdd10 100644 --- a/+tests/+util/getPythonPath.m +++ b/+tests/+util/getPythonPath.m @@ -1,7 +1,7 @@ function pythonPath = getPythonPath() envPath = fullfile('+tests', 'env.mat'); - if isfile(envPath) + if isfile(fullfile(misc.getMatnwbDir, envPath)) Env = load(envPath, '-mat'); if isfield(Env, 'pythonPath') pythonPath = Env.pythonPath; diff --git a/tutorials/html/ophys.html b/tutorials/html/ophys.html index c9cab142..8290f7c9 100644 --- a/tutorials/html/ophys.html +++ b/tutorials/html/ophys.html @@ -39,11 +39,10 @@ .S12 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 18px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 17px; font-weight: 700; text-align: left; } .S13 { margin: 2px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: center; } .S14 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); 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MatNWB Optical Physiology Tutorial

Table of Contents
MatNWB Optical Physiology Tutorial - Introduction - Set up the NWB file - Optical Physiology - Imaging Plane - Storing Two-Photon Data - Storing One-Photon Data - Plane Segmentation - Regions of interest (ROIs) - Adding ROIs to NWB file - Storing fluorescence of ROIs over time - Writing the NWB file - Reading the NWB file -Learn more! - See the API documentation to learn what data types are available. - Other MatNWB tutorials - Python tutorials

Introduction

In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:
It is recommended to first work through the Introduction to MatNWB tutorial, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.

Set up the NWB file

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new NWBFile object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'LastName, FirstName', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
nwb =
NwbFile with properties: - - nwb_version: '2.6.0' +.S19 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 0px none rgb(33, 33, 33); border-radius: 0px; padding: 6px 45px 0px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; } +.S20 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left; }

MatNWB Optical Physiology Tutorial

Table of Contents
MatNWB Optical Physiology Tutorial + Introduction + Set up the NWB file + Optical Physiology + Imaging Plane + Storing Two-Photon Data + Storing One-Photon Data + Motion Correction (optional) + Plane Segmentation + Regions of interest (ROIs) + Adding ROIs to NWB file + Storing fluorescence of ROIs over time + Writing the NWB file + Reading the NWB file +Learn more! + See the API documentation to learn what data types are available. + Other MatNWB tutorials + Python tutorials

Introduction

In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:
  • Acquired two-photon images
  • Image segmentation (ROIs)
  • Fluorescence and dF/F response
It is recommended to first work through the Introduction to MatNWB tutorial, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
Please note: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB data should be specified with time along the last dimension. This is explained in more detail here: MatNWB <-> HDF5 Dimension Mapping.

Set up the NWB file

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new NWBFile object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'LastName, FirstName', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
nwb =
NwbFile with properties: + + nwb_version: '2.7.0' file_create_date: [] identifier: 'Mouse5_Day3' session_description: 'mouse in open exploration' @@ -137,19 +138,14 @@ stimulus_presentation: [0×1 types.untyped.Set] stimulus_templates: [0×1 types.untyped.Set] units: [] -

Optical Physiology

Optical physiology results are written in four steps:
  1. Create imaging plane
  2. Acquired two-photon images
  3. Image segmentation
  4. Fluorescence and dF/F responses

Imaging Plane

First, you must create an ImagingPlane object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a Device object for the microscope and an OpticalChannel object. Then you can create an ImagingPlane.
optical_channel = types.core.OpticalChannel( ...
'description', 'description', ...
'emission_lambda', 500.);
 
device = types.core.Device();
nwb.general_devices.set('Device', device);
 
imaging_plane_name = 'imaging_plane';
imaging_plane = types.core.ImagingPlane( ...
'optical_channel', optical_channel, ...
'description', 'a very interesting part of the brain', ...
'device', types.untyped.SoftLink(device), ...
'excitation_lambda', 600., ...
'imaging_rate', 5., ...
'indicator', 'GFP', ...
'location', 'my favorite brain location');
 
nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);

Storing Two-Photon Data

You can create a TwoPhotonSeries class representing two photon imaging data. TwoPhotonSeries, like SpatialSeries, inherits from TimeSeries and is similar in behavior to OnePhotonSeries.
InternalTwoPhoton = types.core.TwoPhotonSeries( ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'starting_time', 0.0, ...
'starting_time_rate', 3.0, ...
'data', ones(200, 100, 1000), ...
'data_unit', 'lumens');
 
nwb.acquisition.set('2pInternal', InternalTwoPhoton);

Storing One-Photon Data

Now that we have our ImagingPlane, we can create a OnePhotonSeries object to store raw one-photon imaging data.
% using internal data. this data will be stored inside the NWB file
InternalOnePhoton = types.core.OnePhotonSeries( ...
'data', ones(100, 100, 1000), ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'starting_time', 0., ...
'starting_time_rate', 1.0, ...
'data_unit', 'normalized amplitude' ...
);
nwb.acquisition.set('1pInternal', InternalOnePhoton);

Plane Segmentation

Image segmentation stores the detected regions of interest in the TwoPhotonSeries data. ImageSegmentation allows you to have more than one segmentation by creating more PlaneSegmentation objects.

Regions of interest (ROIs)

ROIs can be added to a PlaneSegmentation either as an image_mask or as a pixel_mask. An image mask is an array that is the same size as a single frame of the TwoPhotonSeries, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a ragged array and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the PlaneSegmentation object accordingly.
selection = "Create Image Mask"; % "Create Image Mask" or "Create Pixel Mask"
 
% generate fake image_mask data
imaging_shape = [100, 100];
x = imaging_shape(1);
y = imaging_shape(2);
 
n_rois = 20;
image_mask = zeros(y, x, n_rois);
center = randi(90,2,n_rois);
for i = 1:n_rois
image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;
end
 
if selection == "Create Pixel Mask"
ind = find(image_mask);
[y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);
 
pixel_mask_struct = struct();
pixel_mask_struct.x = uint32(x_ind); % Add x coordinates to struct field x
pixel_mask_struct.y = uint32(y_ind); % Add y coordinates to struct field y
pixel_mask_struct.weight = single(ones(size(x_ind)));
% Create pixel mask vector data
pixel_mask = types.hdmf_common.VectorData(...
'data', struct2table(pixel_mask_struct), ...
'description', 'pixel masks');
 
% When creating a pixel mask, it is also necessary to specify a
% pixel_mask_index vector. See the documentation for ragged arrays linked
% above to learn more.
num_pixels_per_roi = zeros(n_rois, 1); % Column vector
for i_roi = 1:n_rois
num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);
end
 
pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); % Note: Use an integer
% type that can accommodate the maximum value of the cumulative sum
 
% Create pixel_mask_index vector
pixel_mask_index = types.hdmf_common.VectorIndex(...
'description', 'Index into pixel_mask VectorData', ...
'data', pixel_mask_index, ...
'target', types.untyped.ObjectView(pixel_mask) );
 
plane_segmentation = types.core.PlaneSegmentation( ...
'colnames', {'pixel_mask'}, ...
'description', 'roi pixel position (x,y) and pixel weight', ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'pixel_mask_index', pixel_mask_index, ...
'pixel_mask', pixel_mask ...
);
 
else % selection == "Create Image Mask"
plane_segmentation = types.core.PlaneSegmentation( ...
'colnames', {'image_mask'}, ...
'description', 'output from segmenting my favorite imaging plane', ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'image_mask', types.hdmf_common.VectorData(...
'data', image_mask, ...
'description', 'image masks') ...
);
end

Adding ROIs to NWB file

Now create an ImageSegmentation object and put the plane_segmentation object inside of it, naming it PlaneSegmentation.
img_seg = types.core.ImageSegmentation();
img_seg.planesegmentation.set('PlaneSegmentation', plane_segmentation);
Now create a ProcessingModule called "ophys" and put our img_seg object in it, calling it "ImageSegmentation", and add the ProcessingModule to nwb.
ophys_module = types.core.ProcessingModule( ...
'description', 'contains optical physiology data')
ophys_module =
ProcessingModule with properties: - - description: 'contains optical physiology data' - dynamictable: [0×1 types.untyped.Set] - nwbdatainterface: [0×1 types.untyped.Set] -
ophys_module.nwbdatainterface.set('ImageSegmentation', img_seg);
nwb.processing.set('ophys', ophys_module);

Storing fluorescence of ROIs over time

Now that ROIs are stored, you can store fluorescence dF/F data for these regions of interest. This type of data is stored using the RoiResponseSeries class. You will not need to instantiate this class directly to create objects of this type, but it is worth noting that this is the class you will work with after you read data back in.
First, create a data interface to store this data in
roi_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(plane_segmentation), ...
'description', 'all_rois', ...
'data', (0:n_rois-1)');
 
roi_response_series = types.core.RoiResponseSeries( ...
'rois', roi_table_region, ...
'data', NaN(n_rois, 100), ...
'data_unit', 'lumens', ...
'starting_time_rate', 3.0, ...
'starting_time', 0.0);
 
fluorescence = types.core.Fluorescence();
fluorescence.roiresponseseries.set('RoiResponseSeries', roi_response_series);
 
ophys_module.nwbdatainterface.set('Fluorescence', fluorescence);
Finally, the ophys ProcessingModule is added to the NwbFile.
nwb.processing.set('ophys', ophys_module);

Writing the NWB file

nwb_file_name = 'ophys_tutorial.nwb';
if isfile(nwb_file_name); delete(nwb_file_name); end
nwbExport(nwb, nwb_file_name);

Reading the NWB file

read_nwb = nwbRead(nwb_file_name, 'ignorecache');
Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data.
read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence')...
.roiresponseseries.get('RoiResponseSeries').data
ans =
DataStub with properties: +

Optical Physiology

Optical physiology results are written in four steps:
  1. Create imaging plane
  2. Acquired two-photon images
  3. Image segmentation
  4. Fluorescence and dF/F responses

Imaging Plane

First, you must create an ImagingPlane object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a Device object for the microscope and an OpticalChannel object. Then you can create an ImagingPlane.
optical_channel = types.core.OpticalChannel( ...
'description', 'description', ...
'emission_lambda', 500.);
 
device = types.core.Device();
nwb.general_devices.set('Device', device);
 
imaging_plane_name = 'imaging_plane';
imaging_plane = types.core.ImagingPlane( ...
'optical_channel', optical_channel, ...
'description', 'a very interesting part of the brain', ...
'device', types.untyped.SoftLink(device), ...
'excitation_lambda', 600., ...
'imaging_rate', 5., ...
'indicator', 'GFP', ...
'location', 'my favorite brain location');
 
nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);

Storing Two-Photon Data

You can create a TwoPhotonSeries class representing two photon imaging data. TwoPhotonSeries, like SpatialSeries, inherits from TimeSeries and is similar in behavior to OnePhotonSeries.
InternalTwoPhoton = types.core.TwoPhotonSeries( ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'starting_time', 0.0, ...
'starting_time_rate', 3.0, ...
'data', ones(200, 100, 1000), ...
'data_unit', 'lumens');
 
nwb.acquisition.set('2pInternal', InternalTwoPhoton);

Storing One-Photon Data

Now that we have our ImagingPlane, we can create a OnePhotonSeries object to store raw one-photon imaging data.
% using internal data. this data will be stored inside the NWB file
InternalOnePhoton = types.core.OnePhotonSeries( ...
'data', ones(100, 100, 1000), ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'starting_time', 0., ...
'starting_time_rate', 1.0, ...
'data_unit', 'normalized amplitude' ...
);
nwb.acquisition.set('1pInternal', InternalOnePhoton);

Motion Correction (optional)

You can also store the result of motion correction using a MotionCorrection object, a container type that can hold one or more CorrectedImageStack objects.
% Create the corrected ImageSeries
corrected = types.core.ImageSeries( ...
'description', 'A motion corrected image stack', ...
'data', ones(100, 100, 1000), ... % 3D data array
'data_unit', 'n/a', ...
'format', 'raw', ...
'starting_time', 0.0, ...
'starting_time_rate', 1.0 ...
);
 
% Create the xy_translation TimeSeries
xy_translation = types.core.TimeSeries( ...
'description', 'x,y translation in pixels', ...
'data', ones(2, 1000), ... % 2D data array
'data_unit', 'pixels', ...
'starting_time', 0.0, ...
'starting_time_rate', 1.0 ...
);
 
% Create the CorrectedImageStack
corrected_image_stack = types.core.CorrectedImageStack( ...
'corrected', corrected, ...
'original', types.untyped.SoftLink(InternalOnePhoton), ... % Ensure `InternalOnePhoton` exists
'xy_translation', xy_translation ...
);
 
% Create the MotionCorrection object
motion_correction = types.core.MotionCorrection();
motion_correction.correctedimagestack.set('CorrectedImageStack', corrected_image_stack);
The motion corrected data is considered processed data and will be added to the processing field of the nwb object using a ProcessingModule called "ophys". First, create the ProcessingModule object and then add the motion_correction object to it, naming it "MotionCorrection".
ophys_module = types.core.ProcessingModule( ...
'description', 'Contains optical physiology data');
ophys_module.nwbdatainterface.set('MotionCorrection', motion_correction);
Finally, add the "ophys" ProcessingModule to the nwb (Note that we can continue adding objects to the "ophys" ProcessingModule without needing to explicitly update the nwb):
nwb.processing.set('ophys', ophys_module);

Plane Segmentation

Image segmentation stores the detected regions of interest in the TwoPhotonSeries data. ImageSegmentation allows you to have more than one segmentation by creating more PlaneSegmentation objects.

Regions of interest (ROIs)

ROIs can be added to a PlaneSegmentation either as an image_mask or as a pixel_mask. An image mask is an array that is the same size as a single frame of the TwoPhotonSeries, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a ragged array and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the PlaneSegmentation object accordingly.
selection = "Create Image Mask"; % "Create Image Mask" or "Create Pixel Mask"
 
% generate fake image_mask data
imaging_shape = [100, 100];
x = imaging_shape(1);
y = imaging_shape(2);
 
n_rois = 20;
image_mask = zeros(y, x, n_rois);
center = randi(90,2,n_rois);
for i = 1:n_rois
image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;
end
 
if selection == "Create Pixel Mask"
ind = find(image_mask);
[y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);
 
pixel_mask_struct = struct();
pixel_mask_struct.x = uint32(x_ind); % Add x coordinates to struct field x
pixel_mask_struct.y = uint32(y_ind); % Add y coordinates to struct field y
pixel_mask_struct.weight = single(ones(size(x_ind)));
% Create pixel mask vector data
pixel_mask = types.hdmf_common.VectorData(...
'data', struct2table(pixel_mask_struct), ...
'description', 'pixel masks');
 
% When creating a pixel mask, it is also necessary to specify a
% pixel_mask_index vector. See the documentation for ragged arrays linked
% above to learn more.
num_pixels_per_roi = zeros(n_rois, 1); % Column vector
for i_roi = 1:n_rois
num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);
end
 
pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); % Note: Use an integer
% type that can accommodate the maximum value of the cumulative sum
 
% Create pixel_mask_index vector
pixel_mask_index = types.hdmf_common.VectorIndex(...
'description', 'Index into pixel_mask VectorData', ...
'data', pixel_mask_index, ...
'target', types.untyped.ObjectView(pixel_mask) );
 
plane_segmentation = types.core.PlaneSegmentation( ...
'colnames', {'pixel_mask'}, ...
'description', 'roi pixel position (x,y) and pixel weight', ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'pixel_mask_index', pixel_mask_index, ...
'pixel_mask', pixel_mask ...
);
 
else % selection == "Create Image Mask"
plane_segmentation = types.core.PlaneSegmentation( ...
'colnames', {'image_mask'}, ...
'description', 'output from segmenting my favorite imaging plane', ...
'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
'image_mask', types.hdmf_common.VectorData(...
'data', image_mask, ...
'description', 'image masks') ...
);
end

Adding ROIs to NWB file

Now create an ImageSegmentation object and put the plane_segmentation object inside of it, naming it "PlaneSegmentation".
img_seg = types.core.ImageSegmentation();
img_seg.planesegmentation.set('PlaneSegmentation', plane_segmentation);
Add the img_seg object to the "ophys" ProcessingModule we created before, naming it "ImageSegmentation".
ophys_module.nwbdatainterface.set('ImageSegmentation', img_seg);

Storing fluorescence of ROIs over time

Now that ROIs are stored, you can store fluorescence data for these regions of interest. This type of data is stored using the RoiResponseSeries class.
To create a RoiResponseSeries object, we will need to reference a set of rows from the PlaneSegmentation table to indicate which ROIs correspond to which rows of your recorded data matrix. This is done using a DynamicTableRegion, which is a type of link that allows you to reference specific rows of a DynamicTable, such as a PlaneSegmentation table by row indices.
First, we create a DynamicTableRegion that references the ROIs of the PlaneSegmentation table.
roi_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(plane_segmentation), ...
'description', 'all_rois', ...
'data', (0:n_rois-1)');
Then we create a RoiResponseSeries object to store fluorescence data for those ROIs.
roi_response_series = types.core.RoiResponseSeries( ...
'rois', roi_table_region, ...
'data', NaN(n_rois, 100), ... % [nRoi, nT]
'data_unit', 'lumens', ...
'starting_time_rate', 3.0, ...
'starting_time', 0.0);
To help data analysis and visualization tools know that this RoiResponseSeries object represents fluorescence data, we will store the RoiResponseSeries object inside of a Fluorescence object. Then we add the Fluorescence object into the same ProcessingModule named "ophys" that we created earlier.
fluorescence = types.core.Fluorescence();
fluorescence.roiresponseseries.set('RoiResponseSeries', roi_response_series);
 
ophys_module.nwbdatainterface.set('Fluorescence', fluorescence);
Tip: If you want to store dF/F data instead of fluorescence data, then store the RoiResponseSeries object in a DfOverF object, which works the same way as the Fluorescence class.

Writing the NWB file

nwb_file_name = 'ophys_tutorial.nwb';
if isfile(nwb_file_name); delete(nwb_file_name); end
nwbExport(nwb, nwb_file_name);
Warning: The property "grid_spacing_unit" of type "types.core.ImagingPlane" was not exported to file location "/general/optophysiology/imaging_plane" because it depends on the property "grid_spacing" which is unset.
Warning: The property "origin_coords_unit" of type "types.core.ImagingPlane" was not exported to file location "/general/optophysiology/imaging_plane" because it depends on the property "origin_coords" which is unset.

Reading the NWB file

read_nwb = nwbRead(nwb_file_name, 'ignorecache');
Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data.
read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence')...
.roiresponseseries.get('RoiResponseSeries').data
ans =
DataStub with properties: filename: 'ophys_tutorial.nwb' path: '/processing/ophys/Fluorescence/RoiResponseSeries/data' dims: [20 100] ndims: 2 dataType: 'double' -
This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the load method.
load with no input arguments reads the entire dataset:
read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
roiresponseseries.get('RoiResponseSeries').data.load
ans = 20×100
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN +
This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the load method.
load with no input arguments reads the entire dataset:
read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
roiresponseseries.get('RoiResponseSeries').data.load
ans = 20×100
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN @@ -159,12 +155,12 @@ NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN -
If all you need is a section of the data, you can read only that section by indexing the DataStub object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.
read_nwb.processing.get('ophys'). ...
nwbdatainterface.get('Fluorescence'). ...
roiresponseseries.get('RoiResponseSeries'). ...
data(1:5, 1:10)
ans = 5×10
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN +
If all you need is a section of the data, you can read only that section by indexing the DataStub object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.
read_nwb.processing.get('ophys'). ...
nwbdatainterface.get('Fluorescence'). ...
roiresponseseries.get('RoiResponseSeries'). ...
data(1:5, 1:10)
ans = 5×10
NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN -
% read back the image/pixel masks and display the first roi
plane_segmentation = read_nwb.processing.get('ophys'). ...
nwbdatainterface.get('ImageSegmentation'). ...
planesegmentation.get('PlaneSegmentation');
 
if ~isempty(plane_segmentation.image_mask)
roi_mask = plane_segmentation.image_mask.data(:,:,1);
elseif ~isempty(plane_segmentation.pixel_mask)
row = plane_segmentation.getRow(1, 'columns', {'pixel_mask'});
pixel_mask = row.pixel_mask{1};
roi_mask = zeros(imaging_shape);
ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
roi_mask(ind) = pixel_mask.weight;
end
imshow(roi_mask)

Learn more!

See the API documentation to learn what data types are available.

Other MatNWB tutorials

Python tutorials

See our tutorials for more details about your data type:
Check out other tutorials that teach advanced NWB topics:

+
% read back the image/pixel masks and display the first roi
plane_segmentation = read_nwb.processing.get('ophys'). ...
nwbdatainterface.get('ImageSegmentation'). ...
planesegmentation.get('PlaneSegmentation');
 
if ~isempty(plane_segmentation.image_mask)
roi_mask = plane_segmentation.image_mask.data(:,:,1);
elseif ~isempty(plane_segmentation.pixel_mask)
row = plane_segmentation.getRow(1, 'columns', {'pixel_mask'});
pixel_mask = row.pixel_mask{1};
roi_mask = zeros(imaging_shape);
ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
roi_mask(ind) = pixel_mask.weight;
end
imshow(roi_mask)

Learn more!

See the API documentation to learn what data types are available.

Other MatNWB tutorials

Python tutorials

See our tutorials for more details about your data type:
Check out other tutorials that teach advanced NWB topics:


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b/tutorials/private/mcode/intro.m index 175b0880..417c24f8 100644 --- a/tutorials/private/mcode/intro.m +++ b/tutorials/private/mcode/intro.m @@ -125,9 +125,10 @@ % % Note: These diagrams follow a standard convention called "UML class diagram" % to express the object-oriented relationships between NWB classes. For our purposes, -% all you need to know is that an open triangle means "extends" and an open diamond -% means "is contained within." Learn more about class diagrams on . +% all you need to know is that an open triangle means "extends" (i.e., is a specialized +% subtype of), and an open diamond means "is contained within." Learn more about +% class diagrams on . % % is a subclass of object to the module. % create processing module -behavior_mod = types.core.ProcessingModule('description', 'contains behavioral data'); +behavior_module = types.core.ProcessingModule('description', 'contains behavioral data'); -% add the Position object (that holds the SpatialSeries object) to the -% module and name the Position object "Position" -behavior_mod.nwbdatainterface.set('Position', Position); +% add the Position object (that holds the SpatialSeries object) to the module +% and name the Position object "Position" +behavior_module.nwbdatainterface.set('Position', position); % add the processing module to the NWBFile object, and name the processing module "behavior" -nwb.processing.set('behavior', behavior_mod); +nwb.processing.set('behavior', behavior_module); % Trials % Trials are stored in a object which is a subclass of %% % See the to learn what data types are available. \ No newline at end of file +% documentation> to learn what data types are available. +% +% \ No newline at end of file From 63a7c4eac97b75695b6a49656e46435fb22d7d12 Mon Sep 17 00:00:00 2001 From: ehennestad Date: Mon, 25 Nov 2024 14:51:18 +0100 Subject: [PATCH 4/6] Add DecompositionSeries example to ecephys tutorial (#623) * Fix bug in table2nwb * Add DecompositionSeries to ecephys tutorial * Merge ecepys tutorial * Revert changes committed by mistake * Add band_mean and stdev to bands in DecompositionSeries * Properly incorporated changes from main * Suppress output of feature variable + minor fix of TutorialTest --- +tests/+unit/TutorialTest.m | 3 +- +util/table2nwb.m | 4 +- tutorials/ecephys.mlx | Bin 359611 -> 360288 bytes tutorials/html/ecephys.html | 1743 +++++++++++++++-------------- tutorials/private/mcode/ecephys.m | 60 +- 5 files changed, 947 insertions(+), 863 deletions(-) diff --git a/+tests/+unit/TutorialTest.m b/+tests/+unit/TutorialTest.m index 089a55b7..554c0cb2 100644 --- a/+tests/+unit/TutorialTest.m +++ b/+tests/+unit/TutorialTest.m @@ -120,7 +120,8 @@ function inspectTutorialFileWithNwbInspector(testCase) results = py.list(py.nwbinspector.inspect_nwbfile(nwbfile_path=nwbFilename)); results = testCase.convertNwbInspectorResultsToStruct(results); elseif testCase.NWBInspectorMode == "CLI" - [~, m] = system(sprintf('nwbinspector %s --levels importance', nwbFilename)); + [s, m] = system(sprintf('nwbinspector %s --levels importance', nwbFilename)); + testCase.assertEqual(s,0, 'Failed to run NWB Inspector using system command.') results = testCase.parseNWBInspectorTextOutput(m); end diff --git a/+util/table2nwb.m b/+util/table2nwb.m index b2513b94..98553cb3 100644 --- a/+util/table2nwb.m +++ b/+util/table2nwb.m @@ -28,8 +28,8 @@ for col = T if ~strcmp(col.Properties.VariableNames{1},'id') - - if ~isempty(col.Properties.VariableDescriptions{1}) + if ~isempty(col.Properties.VariableDescriptions) ... + && ~isempty(col.Properties.VariableDescriptions{1}) 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Neurodata Without Borders Extracellular Electrophysiology Tutorial

Table of Contents
Neurodata Without Borders Extracellular Electrophysiology Tutorial +.S22 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left; }

Neurodata Without Borders Extracellular Electrophysiology Tutorial

Table of Contents
Neurodata Without Borders Extracellular Electrophysiology Tutorial About This Tutorial Before You Begin - Setting up the NWB File + Setting up the NWB File Electrode Information Electrodes Table Links @@ -89,7 +89,8 @@ Raw Voltage Data Processed Extracellular Electrical Signals LFP - Other Types of Filtered Electrical Signals + Other Types of Filtered Electrical Signals + Decomposition of LFP Data into Frequency Bands Spike Times and Extracellular Events Sorted Spike Times Ragged Arrays @@ -97,13 +98,13 @@ Detected Events Storing Spike Features (e.g Principal Components) Choosing NWB-Types for Electrophysiology Data (A Summary) - Writing the NWB File - Reading NWB Data - Accessing Data Regions -Learn more! - See the API documentation to learn what data types are available. - MATLAB tutorials - Python tutorials

About This Tutorial

This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:
  • Raw voltage recording
  • Local field potential (LFP) and filtered electrical signals
  • Spike times

Before You Begin

It is recommended to first work through the Introduction to MatNWB tutorial, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
Important: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: MatNWB <-> HDF5 Dimension Mapping.

Setting up the NWB File

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session_start_time. Create a new NWBFile object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'Last Name, First Name', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
nwb =

About This Tutorial

This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:
  • Raw voltage recording
  • Local field potential (LFP) and filtered electrical signals
  • Spike times

Before You Begin

It is recommended to first work through the Introduction to MatNWB tutorial, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
Important: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: MatNWB <-> HDF5 Dimension Mapping.

Setting up the NWB File

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session_start_time. Create a new NWBFile object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'Last Name, First Name', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
nwb =
NwbFile with properties: nwb_version: '2.7.0' file_create_date: [] @@ -154,922 +155,900 @@ stimulus_presentation: [0×1 types.untyped.Set] stimulus_templates: [0×1 types.untyped.Set] units: [] -

Electrode Information

In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an electrodes table, which is also a DynamicTable. electrodes has several required fields: x, y, z, impedance, location, filtering, and electrode_group.

Electrodes Table

Since this is a DynamicTable, we can add additional metadata fields. We will be adding a "label" column to the table.
numShanks = 4;
numChannelsPerShank = 3;
numChannels = numShanks * numChannelsPerShank;
 
electrodesDynamicTable = types.hdmf_common.DynamicTable(...
'colnames', {'location', 'group', 'group_name', 'label'}, ...
'description', 'all electrodes');
 
device = types.core.Device(...
'description', 'the best array', ...
'manufacturer', 'Probe Company 9000' ...
);
nwb.general_devices.set('array', device);
for iShank = 1:numShanks
shankGroupName = sprintf('shank%d', iShank);
electrodeGroup = types.core.ElectrodeGroup( ...
'description', sprintf('electrode group for %s', shankGroupName), ...
'location', 'brain area', ...
'device', types.untyped.SoftLink(device) ...
);
nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);
for iElectrode = 1:numChannelsPerShank
electrodesDynamicTable.addRow( ...
'location', 'unknown', ...
'group', types.untyped.ObjectView(electrodeGroup), ...
'group_name', shankGroupName, ...
'label', sprintf('%s-electrode%d', shankGroupName, iElectrode));
end
end
electrodesDynamicTable.toTable() % Display the table
ans = 12×5 table
 idlocationgroupgroup_namelabel
10'unknown'1×1 ObjectView'shank1''shank1-electrode1'
21'unknown'1×1 ObjectView'shank1''shank1-electrode2'
32'unknown'1×1 ObjectView'shank1''shank1-electrode3'
43'unknown'1×1 ObjectView'shank2''shank2-electrode1'
54'unknown'1×1 ObjectView'shank2''shank2-electrode2'
65'unknown'1×1 ObjectView'shank2''shank2-electrode3'
76'unknown'1×1 ObjectView'shank3''shank3-electrode1'
87'unknown'1×1 ObjectView'shank3''shank3-electrode2'
98'unknown'1×1 ObjectView'shank3''shank3-electrode3'
109'unknown'1×1 ObjectView'shank4''shank4-electrode1'
1110'unknown'1×1 ObjectView'shank4''shank4-electrode2'
1211'unknown'1×1 ObjectView'shank4''shank4-electrode3'
nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;

Links

In the above loop, we create ElectrodeGroup objects. The electrodes table then uses an ObjectView in each row to link to the corresponding ElectrodeGroup object. An ObjectView is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.

Recorded Extracellular Signals

Voltage data are stored using the ElectricalSeries class, a subclass of the TimeSeries class specialized for voltage data.

Referencing Electrodes

In order to create our ElectricalSeries object, we first need to reference a set of rows in the electrodes table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a DynamicTableRegion, which is a type of link that allows you to reference specific rows of a DynamicTable, such as the electrodes table, using row indices.
Create a DynamicTableRegion that references all rows of the electrodes table.
electrode_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'all electrodes', ...
'data', (0:length(electrodesDynamicTable.id.data)-1)');

Raw Voltage Data

Now create an ElectricalSeries object to hold acquisition data collected during the experiment.
raw_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 30000., ... % Hz
'data', randn(numChannels, 3000), ... % nChannels x nTime
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.
nwb.acquisition.set('ElectricalSeries', raw_electrical_series);

Processed Extracellular Electrical Signals

LFP

LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an ElectricalSeries. To help data analysis and visualization tools know that this ElectricalSeries object represents LFP data, we store it inside an LFP object and then place the LFP object in a ProcessingModule named 'ecephys'. This is analogous to how we stored the SpatialSeries object inside of a Position object and stored the Position object in a ProcessingModule named 'behavior' in the behavior tutorial
lfp_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000., ... % Hz
'data', randn(numChannels, 100), ... nChannels x nTime
'filtering', 'Low-pass filter at 300 Hz', ...
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
 
lfp = types.core.LFP('ElectricalSeries', lfp_electrical_series);
 
ecephys_module = types.core.ProcessingModule(...
'description', 'extracellular electrophysiology');
 
ecephys_module.nwbdatainterface.set('LFP', lfp);
nwb.processing.set('ecephys', ecephys_module);

Other Types of Filtered Electrical Signals

If your derived data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you should store it in an ElectricalSeries and encapsulate it within a FilteredEphys object instead of the LFP object.
% Generate filtered data
filtered_data = randn(50, 12); % 50 time points, 12 channels
filtered_data = permute(filtered_data, [2, 1]); % permute timeseries for matnwb
 
% Create an ElectricalSeries object
filtered_electrical_series = types.core.ElectricalSeries( ...
'description', 'Data filtered in the Theta range', ...
'data', filtered_data, ...
'electrodes', electrode_table_region, ...
'filtering', 'Band-pass filtered between 4 and 8 Hz', ...
'starting_time', 0.0, ...
'starting_time_rate', 200.0 ...
);
 
% Create a FilteredEphys object and add the filtered electrical series
filtered_ephys = types.core.FilteredEphys();
filtered_ephys.electricalseries.set('FilteredElectricalSeries', filtered_electrical_series);
 
% Add the FilteredEphys object to the ecephys module
ecephys_module.nwbdatainterface.set('FilteredEphys', filtered_ephys);

Spike Times and Extracellular Events

Sorted Spike Times

Spike times are stored in a Units table, a specialization of the DynamicTable class. The default Units table is located at /units in the HDF5 file. You can add columns to the Units table just like you did for electrodes and trials (see convertTrials). Here, we generate some random spike data and populate the table.
num_cells = 10;
spikes = cell(1, num_cells);
for iShank = 1:num_cells
spikes{iShank} = rand(1, randi([16, 28]));
end
spikes
spikes = 1×10 cell
 12345678910
11×23 double1×24 double1×25 double1×27 double1×23 double1×21 double1×27 double1×27 double1×28 double1×28 double

Ragged Arrays

Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the Units table. These indexed columns have two components, the VectorData object that holds the data and the VectorIndex object that holds the indices in the vector that indicate the row breaks. You can use the convenience function util.create_indexed_column to create these objects. For more information about ragged arrays, we refer you to the "Ragged Array Columns" section of the dynamic table tutorial.
[spike_times_vector, spike_times_index] = util.create_indexed_column(spikes);
 
nwb.units = types.core.Units( ...
'colnames', {'spike_times'}, ...
'description', 'units table', ...
'spike_times', spike_times_vector, ...
'spike_times_index', spike_times_index ...
);
 
nwb.units.toTable
ans = 10×2 table
 idspike_times
1123×1 double
2224×1 double
3325×1 double
4427×1 double
5523×1 double
6621×1 double
7727×1 double
8827×1 double
9928×1 double
101028×1 double

Unsorted Spike Times

While the Units table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using SpikeEventSeries objects.
% In the SpikeEventSeries the dimensions should be ordered as
% [num_events, num_channels, num_samples].
% Define spike snippets: 20 events, 3 channels, 40 samples per event.
spike_snippets = rand(20, 3, 40);
% Permute spike snippets (See dimensionMapNoDataPipes tutorial)
spike_snippets = permute(spike_snippets, [3,2,1])
spike_snippets =
spike_snippets(:,:,1) = - - 0.2780 0.0148 0.4113 - 0.9089 0.2536 0.2512 - 0.2961 0.4550 0.8541 - 0.5709 0.1663 0.1813 - 0.6639 0.8085 0.9136 - 0.1532 0.5275 0.1305 - 0.5169 0.6597 0.9037 - 0.6069 0.9045 0.9244 - 0.9657 0.3104 0.8828 - 0.4293 0.2333 0.9000 - 0.3021 0.8831 0.9400 - 0.1023 0.8102 0.2832 - 0.5171 0.9477 0.6370 - 0.6029 0.9545 0.8187 - 0.5206 0.4737 0.8799 - 0.3451 0.1380 0.5775 - 0.0495 0.1734 0.9920 - 0.8352 0.1176 0.1876 - 0.8302 0.8044 0.6006 - 0.3333 0.6354 0.1640 - 0.1174 0.6743 0.8681 - 0.3170 0.6718 0.1311 - 0.6166 0.2288 0.7316 - 0.8783 0.7603 0.0001 - 0.2442 0.5209 0.2155 - 0.3402 0.2235 0.5329 - 0.5665 0.0798 0.7609 - 0.5323 0.5660 0.5499 - 0.7377 0.0673 0.3152 - 0.2693 0.4380 0.2733 - 0.9295 0.5399 0.4471 - 0.2433 0.3025 0.8800 - 0.5839 0.5657 0.5774 - 0.9038 0.2999 0.9696 - 0.6991 0.2018 0.5173 - 0.2515 0.0586 0.2742 - 0.4466 0.4422 0.5018 - 0.9849 0.9096 0.8287 - 0.1720 0.6847 0.6424 - 0.1289 0.0637 0.0174 +

Electrode Information

In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an electrodes table, which is also a DynamicTable. electrodes has several required fields: x, y, z, impedance, location, filtering, and electrode_group.

Electrodes Table

Since this is a DynamicTable, we can add additional metadata fields. We will be adding a "label" column to the table.
numShanks = 4;
numChannelsPerShank = 3;
numChannels = numShanks * numChannelsPerShank;
 
electrodesDynamicTable = types.hdmf_common.DynamicTable(...
'colnames', {'location', 'group', 'group_name', 'label'}, ...
'description', 'all electrodes');
 
device = types.core.Device(...
'description', 'the best array', ...
'manufacturer', 'Probe Company 9000' ...
);
nwb.general_devices.set('array', device);
for iShank = 1:numShanks
shankGroupName = sprintf('shank%d', iShank);
electrodeGroup = types.core.ElectrodeGroup( ...
'description', sprintf('electrode group for %s', shankGroupName), ...
'location', 'brain area', ...
'device', types.untyped.SoftLink(device) ...
);
nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);
for iElectrode = 1:numChannelsPerShank
electrodesDynamicTable.addRow( ...
'location', 'unknown', ...
'group', types.untyped.ObjectView(electrodeGroup), ...
'group_name', shankGroupName, ...
'label', sprintf('%s-electrode%d', shankGroupName, iElectrode));
end
end
electrodesDynamicTable.toTable() % Display the table
ans = 12×5 table
 idlocationgroupgroup_namelabel
10'unknown'1×1 ObjectView'shank1''shank1-electrode1'
21'unknown'1×1 ObjectView'shank1''shank1-electrode2'
32'unknown'1×1 ObjectView'shank1''shank1-electrode3'
43'unknown'1×1 ObjectView'shank2''shank2-electrode1'
54'unknown'1×1 ObjectView'shank2''shank2-electrode2'
65'unknown'1×1 ObjectView'shank2''shank2-electrode3'
76'unknown'1×1 ObjectView'shank3''shank3-electrode1'
87'unknown'1×1 ObjectView'shank3''shank3-electrode2'
98'unknown'1×1 ObjectView'shank3''shank3-electrode3'
109'unknown'1×1 ObjectView'shank4''shank4-electrode1'
1110'unknown'1×1 ObjectView'shank4''shank4-electrode2'
1211'unknown'1×1 ObjectView'shank4''shank4-electrode3'
nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;

Links

In the above loop, we create ElectrodeGroup objects. The electrodes table then uses an ObjectView in each row to link to the corresponding ElectrodeGroup object. An ObjectView is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.

Recorded Extracellular Signals

Voltage data are stored using the ElectricalSeries class, a subclass of the TimeSeries class specialized for voltage data.

Referencing Electrodes

In order to create our ElectricalSeries object, we first need to reference a set of rows in the electrodes table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a DynamicTableRegion, which is a type of link that allows you to reference specific rows of a DynamicTable, such as the electrodes table, using row indices.
Create a DynamicTableRegion that references all rows of the electrodes table.
electrode_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'all electrodes', ...
'data', (0:length(electrodesDynamicTable.id.data)-1)');

Raw Voltage Data

Now create an ElectricalSeries object to hold acquisition data collected during the experiment.
raw_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 30000., ... % Hz
'data', randn(numChannels, 3000), ... % nChannels x nTime
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.
nwb.acquisition.set('ElectricalSeries', raw_electrical_series);

Processed Extracellular Electrical Signals

LFP

LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an ElectricalSeries. To help data analysis and visualization tools know that this ElectricalSeries object represents LFP data, we store it inside an LFP object and then place the LFP object in a ProcessingModule named 'ecephys'. This is analogous to how we stored the SpatialSeries object inside of a Position object and stored the Position object in a ProcessingModule named 'behavior' in the behavior tutorial
lfp_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000., ... % Hz
'data', randn(numChannels, 100), ... nChannels x nTime
'filtering', 'Low-pass filter at 300 Hz', ...
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
 
lfp = types.core.LFP('ElectricalSeries', lfp_electrical_series);
 
ecephys_module = types.core.ProcessingModule(...
'description', 'extracellular electrophysiology');
 
ecephys_module.nwbdatainterface.set('LFP', lfp);
nwb.processing.set('ecephys', ecephys_module);

Other Types of Filtered Electrical Signals

If your acquired data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you can store the result in an ElectricalSeries and encapsulate it within a FilteredEphys object instead of the LFP object.
% Generate filtered data
filtered_data = randn(50, 12); % 50 time points, 12 channels
filtered_data = permute(filtered_data, [2, 1]); % permute timeseries for matnwb
 
% Create an ElectricalSeries object
filtered_electrical_series = types.core.ElectricalSeries( ...
'description', 'Data filtered in the theta range', ...
'data', filtered_data, ...
'electrodes', electrode_table_region, ...
'filtering', 'Band-pass filtered between 4 and 8 Hz', ...
'starting_time', 0.0, ...
'starting_time_rate', 200.0 ...
);
 
% Create a FilteredEphys object and add the filtered electrical series
filtered_ephys = types.core.FilteredEphys();
filtered_ephys.electricalseries.set('FilteredElectricalSeries', filtered_electrical_series);
 
% Add the FilteredEphys object to the ecephys module
ecephys_module.nwbdatainterface.set('FilteredEphys', filtered_ephys);

Decomposition of LFP Data into Frequency Bands

In some cases, you may want to further process the LFP data and decompose the signal into different frequency bands for additional downstream analyses. You can then store the processed data from these spectral analyses using a DecompositionSeries object. This object allows you to include metadata about the frequency bands and metric used (e.g., power, phase, amplitude), as well as link the decomposed data to the original TimeSeries signal the data was derived from.
In this tutorial, the examples for FilteredEphys and DecompositionSeries may appear similar. However, the key difference is that DecompositionSeries is specialized for storing the results of spectral analyses of timeseries data in general, whereas FilteredEphys is defined specifically as a container for filtered electrical signals.
Note: When adding data to a DecompositionSeries, the data argument is assumed to be 3D where the first dimension is time, the second dimension is channels, and the third dimension is bands. As mentioned in the beginning of this tutorial, in MatNWB the data needs to be permuted because the dimensions are written to file in reverse order (See the dimensionMapNoDataPipes tutorial)
% Define the frequency bands of interest (in Hz):
band_names = {'theta'; 'beta'; 'gamma'};
band_mean = [8; 21; 55];
band_stdev = [2; 4.5; 12.5];
band_limits = [band_mean - 2*band_stdev, band_mean + 2*band_stdev];
 
% The bands should be added to the DecompositionSeries as a dynamic table
bands = table(band_names, band_mean, band_stdev, band_limits, ...
'VariableNames', {'band_names', 'band_mean', 'band_stdev', 'band_limits'})
bands = 3×4 table
 band_namesband_meanband_stdevband_limits
12
1'theta'82412
2'beta'214.50001230
3'gamma'5512.50003080
 
bands = util.table2nwb( bands );
 
% Generate random phase data for the demonstration.
phase_data = randn(50, 12, numel(band_names)); % 50 samples, 12 channels, 3 frequency bands
phase_data = permute(phase_data, [3,2,1]); % See dimensionMapNoDataPipes tutorial
 
decomp_series = types.core.DecompositionSeries(...
'data', phase_data, ...
'bands', bands, ...
'metric', 'phase', ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000.0, ... % Hz
'source_channels', electrode_table_region, ...
'source_timeseries', lfp_electrical_series);
 
% Add decomposition series to ecephys module
ecephys_module.nwbdatainterface.set('theta', decomp_series);

Spike Times and Extracellular Events

Sorted Spike Times

Spike times are stored in a Units table, a specialization of the DynamicTable class. The default Units table is located at /units in the HDF5 file. You can add columns to the Units table just like you did for electrodes and trials (see convertTrials). Here, we generate some random spike data and populate the table.
num_cells = 10;
spikes = cell(1, num_cells);
for iShank = 1:num_cells
spikes{iShank} = rand(1, randi([16, 28]));
end
spikes
spikes = 1×10 cell
 12345678910
11×24 double1×28 double1×22 double1×28 double1×21 double1×26 double1×16 double1×18 double1×24 double1×24 double

Ragged Arrays

Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the Units table. These indexed columns have two components, the VectorData object that holds the data and the VectorIndex object that holds the indices in the vector that indicate the row breaks. You can use the convenience function util.create_indexed_column to create these objects. For more information about ragged arrays, we refer you to the "Ragged Array Columns" section of the dynamic table tutorial.
[spike_times_vector, spike_times_index] = util.create_indexed_column(spikes);
 
nwb.units = types.core.Units( ...
'colnames', {'spike_times'}, ...
'description', 'units table', ...
'spike_times', spike_times_vector, ...
'spike_times_index', spike_times_index ...
);
 
nwb.units.toTable
ans = 10×2 table
 idspike_times
1124×1 double
2228×1 double
3322×1 double
4428×1 double
5521×1 double
6626×1 double
7716×1 double
8818×1 double
9924×1 double
101024×1 double

Unsorted Spike Times

While the Units table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using SpikeEventSeries objects.
% In the SpikeEventSeries the dimensions should be ordered as
% [num_events, num_channels, num_samples].
% Define spike snippets: 20 events, 3 channels, 40 samples per event.
spike_snippets = rand(20, 3, 40);
% Permute spike snippets (See dimensionMapNoDataPipes tutorial)
spike_snippets = permute(spike_snippets, [3,2,1])
spike_snippets =
spike_snippets(:,:,1) = + + 0.7910 0.0213 0.8335 + 0.8083 0.5109 0.0694 + 0.0414 0.1315 0.9237 + 0.6840 0.0158 0.8003 + 0.9652 0.0518 0.6909 + 0.4137 0.6962 0.6477 + 0.7065 0.4276 0.1037 + 0.0327 0.2995 0.3123 + 0.5643 0.7282 0.7042 + 0.3009 0.8131 0.9222 + 0.1623 0.8546 0.4080 + 0.3810 0.7375 0.5178 + 0.1005 0.8742 0.2635 + 0.9530 0.3118 0.5444 + 0.0115 0.0973 0.5146 + 0.1436 0.2857 0.3713 + 0.5966 0.3174 0.4652 + 0.0131 0.8012 0.6369 + 0.4585 0.6484 0.6865 + 0.5535 0.1510 0.3442 + 0.0254 0.5062 0.8555 + 0.7063 0.5182 0.6404 + 0.3815 0.1169 0.1685 + 0.8255 0.1120 0.3037 + 0.1461 0.4718 0.1424 + 0.8712 0.8092 0.7453 + 0.3177 0.3974 0.9020 + 0.4894 0.6920 0.5508 + 0.1267 0.0666 0.8641 + 0.9720 0.7211 0.6973 + 0.8166 0.9120 0.1665 + 0.4227 0.3168 0.6086 + 0.0780 0.9907 0.1200 + 0.0661 0.3271 0.7940 + 0.8019 0.9782 0.7975 + 0.4981 0.6964 0.9912 + 0.5068 0.4475 0.9353 + 0.4478 0.6194 0.5117 + 0.8898 0.5428 0.7175 + 0.8606 0.2924 0.9609 spike_snippets(:,:,2) = - 0.1356 0.8906 0.1356 - 0.8359 0.4499 0.4325 - 0.5334 0.7425 0.3893 - 0.7806 0.4774 0.1446 - 0.7090 0.6839 0.8727 - 0.6276 0.3848 0.9937 - 0.6387 0.0727 0.0036 - 0.2166 0.6525 0.1900 - 0.7040 0.5891 0.2017 - 0.3564 0.7157 0.4724 - 0.1167 0.6595 0.5219 - 0.2402 0.7465 0.9879 - 0.7433 0.5308 0.0752 - 0.5490 0.8776 0.2981 - 0.0620 0.7035 0.5912 - 0.1362 0.5743 0.8116 - 0.8572 0.4815 0.6027 - 0.9946 0.4654 0.4843 - 0.0120 0.4187 0.2885 - 0.4056 0.3358 0.7233 - 0.9679 0.1308 0.8167 - 0.2359 0.1989 0.8837 - 0.5314 0.6617 0.3106 - 0.0073 0.3747 0.3369 - 0.4491 0.5629 0.3621 - 0.3106 0.3202 0.3869 - 0.6711 0.7039 0.4220 - 0.5631 0.3319 0.3008 - 0.1683 0.4825 0.1346 - 0.2136 0.7076 0.7932 - 0.8566 0.7091 0.5236 - 0.0288 0.6919 0.5200 - 0.7006 0.1266 0.5687 - 0.6473 0.8367 0.3659 - 0.6176 0.6711 0.9105 - 0.9251 0.7086 0.8938 - 0.1278 0.7159 0.8852 - 0.0841 0.6990 0.3079 - 0.0080 0.0005 0.4075 - 0.7984 0.6617 0.3013 + 0.5338 0.3753 0.6193 + 0.1517 0.6080 0.1663 + 0.1934 0.6342 0.9882 + 0.9244 0.8932 0.3000 + 0.8507 0.4194 0.6482 + 0.5106 0.1159 0.7561 + 0.1162 0.3095 0.9177 + 0.9806 0.6443 0.8760 + 0.0513 0.4850 0.9481 + 0.8886 0.2191 0.6419 + 0.6883 0.4286 0.8974 + 0.8499 0.5069 0.4643 + 0.9019 0.3152 0.4006 + 0.2973 0.4501 0.0411 + 0.2026 0.1939 0.9824 + 0.6041 0.2842 0.5462 + 0.9371 0.5834 0.8635 + 0.4799 0.6260 0.2953 + 0.5238 0.7532 0.8461 + 0.6517 0.7502 0.3512 + 0.4229 0.1752 0.1634 + 0.7366 0.7801 0.9180 + 0.1957 0.2408 0.3131 + 0.9544 0.5748 0.1483 + 0.8544 0.0117 0.7080 + 0.7467 0.9576 0.2643 + 0.5438 0.0537 0.0843 + 0.9563 0.6243 0.4454 + 0.5092 0.1294 0.3496 + 0.4297 0.4393 0.2485 + 0.4890 0.8836 0.6625 + 0.1389 0.4318 0.3081 + 0.1869 0.0011 0.4418 + 0.9201 0.4152 0.0008 + 0.4235 0.6870 0.1545 + 0.9545 0.0976 0.9682 + 0.7207 0.6316 0.7971 + 0.9008 0.5584 0.7311 + 0.1453 0.9232 0.3198 + 0.5304 0.4056 0.1549 spike_snippets(:,:,3) = - 0.3783 0.7965 0.1050 - 0.7297 0.1329 0.8280 - 0.0421 0.7440 0.5148 - 0.0433 0.1966 0.3639 - 0.3937 0.0728 0.1104 - 0.5506 0.7925 0.4094 - 0.3709 0.7109 0.0431 - 0.5237 0.6674 0.2444 - 0.1808 0.7575 0.3823 - 0.8111 0.5852 0.2838 - 0.3822 0.9055 0.2303 - 0.0973 0.3953 0.7989 - 0.0419 0.8453 0.6505 - 0.4524 0.3664 0.2062 - 0.4113 0.1807 0.1150 - 0.0886 0.8685 0.3013 - 0.3449 0.5180 0.2821 - 0.3773 0.6463 0.4354 - 0.7497 0.3262 0.9738 - 0.3253 0.4987 0.7777 - 0.8843 0.4842 0.8025 - 0.6655 0.3403 0.4367 - 0.6173 0.6862 0.7745 - 0.3096 0.4351 0.8634 - 0.9581 0.5508 0.9057 - 0.9796 0.8453 0.2989 - 0.4616 0.3215 0.5217 - 0.6465 0.2757 0.8933 - 0.6273 0.7627 0.3487 - 0.7493 0.0711 0.7441 - 0.6469 0.6425 0.3223 - 0.2125 0.5889 0.7047 - 0.3090 0.2534 0.4767 - 0.2812 0.5091 0.6808 - 0.1553 0.4004 0.3360 - 0.5689 0.5763 0.1933 - 0.4353 0.9890 0.3021 - 0.4081 0.5490 0.2611 - 0.0308 0.0775 0.0438 - 0.2588 0.8739 0.3807 + 0.5092 0.9115 0.6848 + 0.0370 0.3598 0.5459 + 0.8909 0.2320 0.6210 + 0.1415 0.3598 0.3532 + 0.6650 0.7777 0.2036 + 0.0845 0.7893 0.6989 + 0.8263 0.5907 0.7732 + 0.7758 0.9209 0.6862 + 0.6141 0.4780 0.6086 + 0.4845 0.5522 0.5599 + 0.9572 0.1676 0.3414 + 0.1539 0.5025 0.8494 + 0.6809 0.5092 0.8636 + 0.9649 0.6492 0.5732 + 0.3245 0.6510 0.9502 + 0.3759 0.3767 0.4420 + 0.7450 0.5653 0.0341 + 0.8066 0.5285 0.6691 + 0.7006 0.0285 0.2575 + 0.4817 0.5484 0.8613 + 0.2185 0.3218 0.5705 + 0.4282 0.1795 0.8811 + 0.3090 0.8398 0.6193 + 0.0477 0.4053 0.8866 + 0.1156 0.5851 0.2542 + 0.8462 0.0158 0.3698 + 0.4941 0.9994 0.2657 + 0.6635 0.6725 0.1791 + 0.8944 0.2112 0.0403 + 0.3573 0.8075 0.2363 + 0.0931 0.0854 0.2321 + 0.4501 0.9337 0.2882 + 0.5619 0.6824 0.8621 + 0.5455 0.0936 0.0346 + 0.4649 0.7467 0.8067 + 0.0441 0.9981 0.1671 + 0.1436 0.4552 0.5099 + 0.9111 0.4916 0.9271 + 0.1548 0.1222 0.4906 + 0.3945 0.8146 0.5500 spike_snippets(:,:,4) = - 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0.5319 0.4611 0.0238 - 0.0107 0.4021 0.9675 - 0.2670 0.4292 0.2256 - 0.0075 0.2988 0.7416 - 0.6676 0.3036 0.8923 - 0.2578 0.9139 0.3128 - 0.4729 0.1512 0.3253 - 0.0726 0.7248 0.6174 - 0.1111 0.1281 0.6482 - 0.1313 0.5600 0.3097 - 0.6213 0.9661 0.2627 - 0.2338 0.6094 0.3207 - 0.1518 0.5683 0.1826 - 0.4942 0.6299 0.3986 - 0.7793 0.3064 0.4732 - 0.6928 0.7628 0.8839 - 0.6003 0.6119 0.9674 - 0.2245 0.4440 0.3893 - 0.3195 0.2501 0.3615 - 0.9588 0.0686 0.9949 - 0.1095 0.7686 0.3792 - 0.6823 0.8999 0.1835 - 0.4789 0.4263 0.1516 - 0.7116 0.3164 0.5941 - 0.1937 0.2194 0.6845 - 0.6531 0.7972 0.8251 - 0.8248 0.3564 0.6396 - 0.9730 0.2390 0.7955 - 0.7474 0.6799 0.7575 - 0.9625 0.1981 0.9859 - 0.2182 0.2432 0.8913 - 0.6330 0.5699 0.0269 - 0.6607 0.3176 0.4056 - 0.1300 0.8386 0.4653 - 0.5340 0.0663 0.1382 - 0.6349 0.7329 0.6797 - 0.0701 0.4965 0.6668 - 0.5020 0.1398 0.9634 - 0.5295 0.0102 0.9432 - 0.4737 0.2624 0.7338 + 0.7833 0.3800 0.2596 + 0.4774 0.3225 0.9132 + 0.3767 0.5766 0.7866 + 0.5167 0.7333 0.2876 + 0.3313 0.6348 0.4921 + 0.9909 0.7142 0.0040 + 0.7992 0.8899 0.7669 + 0.1438 0.3450 0.1557 + 0.8615 0.5972 0.6849 + 0.2221 0.2920 0.8230 + 0.9428 0.8654 0.7775 + 0.5121 0.7407 0.5810 + 0.6163 0.4000 0.3330 + 0.3748 0.9231 0.6470 + 0.4877 0.7523 0.8188 + 0.4864 0.9930 0.0859 + 0.0486 0.4711 0.6532 + 0.3978 0.0564 0.0195 + 0.9749 0.1345 0.4348 + 0.4293 0.0039 0.9382 + 0.5667 0.6412 0.2270 + 0.5779 0.7878 0.6903 + 0.4924 0.9922 0.8492 + 0.6379 0.1296 0.5580 + 0.4197 0.5457 0.1361 + 0.4808 0.5291 0.6389 + 0.6873 0.0123 0.3525 + 0.9886 0.1202 0.7538 + 0.7048 0.8567 0.9505 + 0.3475 0.1045 0.1739 + 0.8209 0.6846 0.5388 + 0.0525 0.3191 0.7253 + 0.1306 0.5241 0.2333 + 0.6714 0.2183 0.1283 + 0.8935 0.1168 0.5805 + 0.8253 0.1419 0.3755 + 0.6320 0.2447 0.7466 + 0.0071 0.0700 0.8273 + 0.3107 0.5529 0.8195 + 0.2268 0.3851 0.4185 spike_snippets(:,:,20) = - 0.6584 0.9637 0.6694 - 0.6882 0.9234 0.5334 - 0.9451 0.2206 0.1100 - 0.1020 0.7264 0.1427 - 0.8448 0.6778 0.2754 - 0.1440 0.4233 0.8132 - 0.5736 0.6490 0.4518 - 0.2343 0.2426 0.4747 - 0.0774 0.1005 0.0246 - 0.9198 0.3224 0.4104 - 0.4359 0.3599 0.1246 - 0.5686 0.0664 0.3270 - 0.2882 0.4851 0.5189 - 0.7017 0.9357 0.8863 - 0.3291 0.3256 0.1743 - 0.7455 0.4708 0.5486 - 0.6446 0.0108 0.8551 - 0.4382 0.1401 0.4226 - 0.2296 0.6263 0.9103 - 0.2811 0.5259 0.3679 - 0.6857 0.9481 0.0568 - 0.2926 0.8703 0.7404 - 0.8050 0.0736 0.1273 - 0.7048 0.6245 0.4469 - 0.9038 0.0299 0.8430 - 0.5372 0.8920 0.6377 - 0.9344 0.5897 0.1500 - 0.5390 0.3147 0.9303 - 0.6639 0.7378 0.5821 - 0.7281 0.8026 0.4448 - 0.2334 0.8817 0.7433 - 0.4686 0.5002 0.2370 - 0.8984 0.6981 0.5906 - 0.4098 0.2956 0.0880 - 0.2328 0.6203 0.4738 - 0.6246 0.7486 0.9808 - 0.2156 0.1182 0.3692 - 0.6777 0.0449 0.6831 - 0.3080 0.6611 0.3553 - 0.0171 0.1520 0.3349 -
 
% Create electrode table region referencing electrodes 0, 1, and 2
shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'shank0', ...
'data', (0:2)');
 
% Define spike event series for unsorted spike times
spike_events = types.core.SpikeEventSeries( ...
'data', spike_snippets, ...
'timestamps', (0:19)', ... % Timestamps for each event
'description', 'events detected with 100uV threshold', ...
'electrodes', shank0_table_region ...
);
 
% Add spike event series to NWB file acquisition
nwb.acquisition.set('SpikeEvents_Shank0', spike_events);

Detected Events

If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in ElectricalSeries objects in the acquisition group, and use the EventDetection class to identify the spike events in your raw traces.
% Create the EventDetection object
event_detection = types.core.EventDetection( ...
'detection_method', 'thresholding, 1.5 * std', ...
'source_electricalseries', types.untyped.SoftLink(raw_electrical_series), ...
'source_idx', [1000; 2000; 3000], ...
'times', [.033, .066, .099] ...
);
 
% Add the EventDetection object to the ecephys module
ecephys_module.nwbdatainterface.set('ThresholdEvents', event_detection);

Storing Spike Features (e.g Principal Components)

NWB also provides a way to store features of spikes, such as principal components, using the FeatureExtraction class.
% Generate random feature data (time x channel x feature)
features = rand(3, 12, 4); % 3 time points, 12 channels, 4 features
features = permute(features, [3,2,1]) % reverse dimension order for matnwb
features =
features(:,:,1) = - - 0.9316 0.4439 0.1664 0.8142 0.2697 0.6258 0.4445 0.5889 0.5936 0.5510 0.3843 0.0928 - 0.6321 0.6096 0.2896 0.5378 0.2169 0.2326 0.6727 0.3081 0.5352 0.3272 0.3730 0.7105 - 0.5674 0.5495 0.8397 0.5804 0.3298 0.0668 0.6826 0.1170 0.2237 0.8309 0.8602 0.1544 - 0.4657 0.4697 0.6416 0.8899 0.2070 0.2748 0.1939 0.7873 0.9721 0.7856 0.0667 0.1039 - - -features(:,:,2) = - - 0.9188 0.8798 0.8666 0.2478 0.7362 0.0880 0.0963 0.1304 0.5751 0.4159 0.8664 0.6476 - 0.9597 0.7697 0.0909 0.7685 0.0993 0.9162 0.8964 0.5880 0.4342 0.1795 0.7080 0.4490 - 0.5198 0.6699 0.2718 0.2714 0.3393 0.5473 0.2908 0.5508 0.2389 0.9618 0.6670 0.1449 - 0.1393 0.5076 0.7794 0.9437 0.9352 0.1637 0.5838 0.1138 0.7266 0.7907 0.8624 0.1127 - - -features(:,:,3) = - - 0.8750 0.5151 0.1783 0.6123 0.1882 0.2455 0.3716 0.9768 0.1423 0.1525 0.3370 0.2588 - 0.4765 0.7175 0.5126 0.4949 0.2113 0.9030 0.0051 0.3652 0.7116 0.9779 0.8704 0.7190 - 0.6226 0.7981 0.6252 0.9443 0.8116 0.8497 0.9575 0.3736 0.2381 0.6094 0.1856 0.6485 - 0.8744 0.2761 0.6534 0.0308 0.2033 0.1817 0.1606 0.0353 0.8287 0.1804 0.5846 0.6030 -
 
% Create the FeatureExtraction object
feature_extraction = types.core.FeatureExtraction( ...
'description', {'PC1', 'PC2', 'PC3', 'PC4'}, ... % Feature descriptions
'electrodes', electrode_table_region, ... % DynamicTableRegion referencing the electrodes table
'times', [.033; .066; .099], ... % Column vector for times
'features', features ...
);
 
% Add the FeatureExtraction object to the ecephys module (if required)
ecephys_module.nwbdatainterface.set('PCA_features', feature_extraction);

Choosing NWB-Types for Electrophysiology Data (A Summary)

As mentioned above, ElectricalSeries objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the ElectricalSeries class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the API documentation and the section on Extracellular Physiology in the "NWB Format Specification" for more details on using these objects.
For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with ElectricalSeries objects as acquisition data, and use the EventDetection class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use SpikeEventSeries objects.
The results of spike sorting (or clustering) should be stored in the top-level Units table. The Units table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns waveform_mean, waveform_sd, and waveforms in the Units table to store individual and mean waveform data.

Writing the NWB File

nwbExport(nwb, 'ecephys_tutorial.nwb')

Reading NWB Data

Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. load with no input arguments reads the entire dataset:
nwb2 = nwbRead('ecephys_tutorial.nwb', 'ignorecache');
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data.load;

Accessing Data Regions

If all you need is a data region, you can index a DataStub object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.
% read section of LFP
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data(1:5, 1:10)
ans = 5×10
-0.0003 -1.3106 2.3180 0.0008 1.9394 1.7373 -1.1235 -0.1723 -0.7690 0.3233 - -2.7453 0.6558 0.7985 -0.7032 -0.1310 0.7679 -1.0497 2.1285 0.8362 0.3742 - 0.7983 1.4374 0.1157 0.9797 0.2739 -1.4714 -2.2506 1.2310 -0.5585 0.0383 - -0.3854 -1.5432 1.3848 0.9771 1.1799 -0.3393 -1.3353 -1.6190 2.2322 -1.1545 - -0.9029 -1.5846 -0.5733 -0.1312 -1.0385 -0.9423 -0.2002 -0.9642 -0.4652 -1.5418 -
 
% You can use the getRow method of the table to load spike times of a specific unit.
% To get the values, unpack from the returned table.
nwb.units.getRow(1).spike_times{1}
ans = 23×1
0.7889 - 0.1248 - 0.0928 - 0.2707 - 0.1974 - 0.6140 - 0.5269 - 0.8578 - 0.2521 - 0.3357 -

Learn more!

See the API documentation to learn what data types are available.

MATLAB tutorials

Python tutorials

See our tutorials for more details about your data type:
Check out other tutorials that teach advanced NWB topics:
+ 0.0803 0.5497 0.8134 + 0.2016 0.7139 0.9475 + 0.2989 0.6889 0.9147 + 0.4506 0.2072 0.5099 + 0.6934 0.8924 0.4041 + 0.9842 0.2183 0.5916 + 0.9099 0.1014 0.2588 + 0.3646 0.2869 0.1107 + 0.7892 0.5949 0.2686 + 0.8226 0.5505 0.3111 + 0.0764 0.4849 0.1924 + 0.2240 0.8471 0.8423 + 0.1020 0.2254 0.4194 + 0.8691 0.5801 0.1725 + 0.8217 0.0235 0.0745 + 0.6285 0.1380 0.2667 + 0.6437 0.4091 0.5609 + 0.6716 0.2623 0.3741 + 0.7291 0.8411 0.0698 + 0.4717 0.0125 0.4050 + 0.1205 0.4536 0.2174 + 0.1712 0.4339 0.4282 + 0.2948 0.1448 0.0394 + 0.8869 0.1695 0.8341 + 0.0986 0.4590 0.6105 + 0.2961 0.1109 0.6225 + 0.2111 0.6054 0.6792 + 0.9609 0.6630 0.4239 + 0.8231 0.0917 0.1793 + 0.9510 0.1655 0.7910 + 0.8841 0.3304 0.3527 + 0.7403 0.3570 0.1069 + 0.6682 0.3612 0.9556 + 0.7742 0.0715 0.6476 + 0.8029 0.4781 0.8936 + 0.8517 0.2079 0.6216 + 0.8403 0.8003 0.7215 + 0.5677 0.7792 0.8469 + 0.1805 0.0260 0.8686 + 0.4809 0.8847 0.0151 +
 
% Create electrode table region referencing electrodes 0, 1, and 2
shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'shank0', ...
'data', (0:2)');
 
% Define spike event series for unsorted spike times
spike_events = types.core.SpikeEventSeries( ...
'data', spike_snippets, ...
'timestamps', (0:19)', ... % Timestamps for each event
'description', 'events detected with 100uV threshold', ...
'electrodes', shank0_table_region ...
);
 
% Add spike event series to NWB file acquisition
nwb.acquisition.set('SpikeEvents_Shank0', spike_events);

Detected Events

If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in ElectricalSeries objects in the acquisition group, and use the EventDetection class to identify the spike events in your raw traces.
% Create the EventDetection object
event_detection = types.core.EventDetection( ...
'detection_method', 'thresholding, 1.5 * std', ...
'source_electricalseries', types.untyped.SoftLink(raw_electrical_series), ...
'source_idx', [1000; 2000; 3000], ...
'times', [.033, .066, .099] ...
);
 
% Add the EventDetection object to the ecephys module
ecephys_module.nwbdatainterface.set('ThresholdEvents', event_detection);

Storing Spike Features (e.g Principal Components)

NWB also provides a way to store features of spikes, such as principal components, using the FeatureExtraction class.
% Generate random feature data (time x channel x feature)
features = rand(3, 12, 4); % 3 time points, 12 channels, 4 features
features = permute(features, [3,2,1]); % reverse dimension order for matnwb
 
% Create the FeatureExtraction object
feature_extraction = types.core.FeatureExtraction( ...
'description', {'PC1', 'PC2', 'PC3', 'PC4'}, ... % Feature descriptions
'electrodes', electrode_table_region, ... % DynamicTableRegion referencing the electrodes table
'times', [.033; .066; .099], ... % Column vector for times
'features', features ...
);
 
% Add the FeatureExtraction object to the ecephys module (if required)
ecephys_module.nwbdatainterface.set('PCA_features', feature_extraction);

Choosing NWB-Types for Electrophysiology Data (A Summary)

As mentioned above, ElectricalSeries objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the ElectricalSeries class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the API documentation and the section on Extracellular Physiology in the "NWB Format Specification" for more details on using these objects.
For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with ElectricalSeries objects as acquisition data, and use the EventDetection class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use SpikeEventSeries objects.
The results of spike sorting (or clustering) should be stored in the top-level Units table. The Units table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns waveform_mean, waveform_sd, and waveforms in the Units table to store individual and mean waveform data.

Writing the NWB File

nwbExport(nwb, 'ecephys_tutorial.nwb')

Reading NWB Data

Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. load with no input arguments reads the entire dataset:
nwb2 = nwbRead('ecephys_tutorial.nwb', 'ignorecache');
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data.load;

Accessing Data Regions

If all you need is a data region, you can index a DataStub object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.
% read section of LFP
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data(1:5, 1:10)
ans = 5×10
2.0421 -1.9417 0.3559 0.4354 0.6993 -1.4009 0.5222 0.0893 0.1243 1.2460 + 0.2329 0.4688 2.1159 1.2094 0.1735 -0.3315 0.1403 -2.0881 0.2840 -0.4077 + -0.0943 1.3933 -0.0871 -0.5193 -0.0920 -1.1307 0.8399 0.0975 -0.6912 -0.4536 + -1.4224 2.7602 1.1832 0.0075 0.6687 0.2074 0.5432 0.4366 0.0113 0.2925 + 0.9660 0.9444 -0.8471 1.0362 0.0652 -0.2155 0.6006 0.1602 0.7417 -1.3644 +
 
% You can use the getRow method of the table to load spike times of a specific unit.
% To get the values, unpack from the returned table.
nwb.units.getRow(1).spike_times{1}
ans = 24×1
0.1312 + 0.4211 + 0.2037 + 0.1021 + 0.1315 + 0.8392 + 0.9195 + 0.7023 + 0.0588 + 0.7331 +

Learn more!

See the API documentation to learn what data types are available.

MATLAB tutorials

Python tutorials

See our tutorials for more details about your data type:
Check out other tutorials that teach advanced NWB topics:

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About This Tutorial

This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:
  • Raw voltage recording
  • Local field potential (LFP) and filtered electrical signals
  • Spike times

Before You Begin

It is recommended to first work through the Introduction to MatNWB tutorial, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
Important: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: MatNWB <-> HDF5 Dimension Mapping.

Setting up the NWB File

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session_start_time. Create a new NWBFile object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'Last Name, First Name', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
nwb =
NwbFile with properties: + Python tutorials

About This Tutorial

This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:
  • Raw voltage recording
  • Local field potential (LFP) and filtered electrical signals
  • Spike times

Before You Begin

It is recommended to first work through the Introduction to MatNWB tutorial, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
Important: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: MatNWB <-> HDF5 Dimension Mapping.

Setting up the NWB File

An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session_start_time. Create a new NWBFile object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.
nwb = NwbFile( ...
'session_description', 'mouse in open exploration',...
'identifier', 'Mouse5_Day3', ...
'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
'general_experimenter', 'Last Name, First Name', ... % optional
'general_session_id', 'session_1234', ... % optional
'general_institution', 'University of My Institution', ... % optional
'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
nwb
nwb =
NwbFile with properties: nwb_version: '2.7.0' file_create_date: [] @@ -155,899 +155,899 @@ stimulus_presentation: [0×1 types.untyped.Set] stimulus_templates: [0×1 types.untyped.Set] units: [] -

Electrode Information

In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an electrodes table, which is also a DynamicTable. electrodes has several required fields: x, y, z, impedance, location, filtering, and electrode_group.

Electrodes Table

Since this is a DynamicTable, we can add additional metadata fields. We will be adding a "label" column to the table.
numShanks = 4;
numChannelsPerShank = 3;
numChannels = numShanks * numChannelsPerShank;
 
electrodesDynamicTable = types.hdmf_common.DynamicTable(...
'colnames', {'location', 'group', 'group_name', 'label'}, ...
'description', 'all electrodes');
 
device = types.core.Device(...
'description', 'the best array', ...
'manufacturer', 'Probe Company 9000' ...
);
nwb.general_devices.set('array', device);
for iShank = 1:numShanks
shankGroupName = sprintf('shank%d', iShank);
electrodeGroup = types.core.ElectrodeGroup( ...
'description', sprintf('electrode group for %s', shankGroupName), ...
'location', 'brain area', ...
'device', types.untyped.SoftLink(device) ...
);
nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);
for iElectrode = 1:numChannelsPerShank
electrodesDynamicTable.addRow( ...
'location', 'unknown', ...
'group', types.untyped.ObjectView(electrodeGroup), ...
'group_name', shankGroupName, ...
'label', sprintf('%s-electrode%d', shankGroupName, iElectrode));
end
end
electrodesDynamicTable.toTable() % Display the table
ans = 12×5 table
 idlocationgroupgroup_namelabel
10'unknown'1×1 ObjectView'shank1''shank1-electrode1'
21'unknown'1×1 ObjectView'shank1''shank1-electrode2'
32'unknown'1×1 ObjectView'shank1''shank1-electrode3'
43'unknown'1×1 ObjectView'shank2''shank2-electrode1'
54'unknown'1×1 ObjectView'shank2''shank2-electrode2'
65'unknown'1×1 ObjectView'shank2''shank2-electrode3'
76'unknown'1×1 ObjectView'shank3''shank3-electrode1'
87'unknown'1×1 ObjectView'shank3''shank3-electrode2'
98'unknown'1×1 ObjectView'shank3''shank3-electrode3'
109'unknown'1×1 ObjectView'shank4''shank4-electrode1'
1110'unknown'1×1 ObjectView'shank4''shank4-electrode2'
1211'unknown'1×1 ObjectView'shank4''shank4-electrode3'
nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;

Links

In the above loop, we create ElectrodeGroup objects. The electrodes table then uses an ObjectView in each row to link to the corresponding ElectrodeGroup object. An ObjectView is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.

Recorded Extracellular Signals

Voltage data are stored using the ElectricalSeries class, a subclass of the TimeSeries class specialized for voltage data.

Referencing Electrodes

In order to create our ElectricalSeries object, we first need to reference a set of rows in the electrodes table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a DynamicTableRegion, which is a type of link that allows you to reference specific rows of a DynamicTable, such as the electrodes table, using row indices.
Create a DynamicTableRegion that references all rows of the electrodes table.
electrode_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'all electrodes', ...
'data', (0:length(electrodesDynamicTable.id.data)-1)');

Raw Voltage Data

Now create an ElectricalSeries object to hold acquisition data collected during the experiment.
raw_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 30000., ... % Hz
'data', randn(numChannels, 3000), ... % nChannels x nTime
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.
nwb.acquisition.set('ElectricalSeries', raw_electrical_series);

Processed Extracellular Electrical Signals

LFP

LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an ElectricalSeries. To help data analysis and visualization tools know that this ElectricalSeries object represents LFP data, we store it inside an LFP object and then place the LFP object in a ProcessingModule named 'ecephys'. This is analogous to how we stored the SpatialSeries object inside of a Position object and stored the Position object in a ProcessingModule named 'behavior' in the behavior tutorial
lfp_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000., ... % Hz
'data', randn(numChannels, 100), ... nChannels x nTime
'filtering', 'Low-pass filter at 300 Hz', ...
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
 
lfp = types.core.LFP('ElectricalSeries', lfp_electrical_series);
 
ecephys_module = types.core.ProcessingModule(...
'description', 'extracellular electrophysiology');
 
ecephys_module.nwbdatainterface.set('LFP', lfp);
nwb.processing.set('ecephys', ecephys_module);

Other Types of Filtered Electrical Signals

If your acquired data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you can store the result in an ElectricalSeries and encapsulate it within a FilteredEphys object instead of the LFP object.
% Generate filtered data
filtered_data = randn(50, 12); % 50 time points, 12 channels
filtered_data = permute(filtered_data, [2, 1]); % permute timeseries for matnwb
 
% Create an ElectricalSeries object
filtered_electrical_series = types.core.ElectricalSeries( ...
'description', 'Data filtered in the theta range', ...
'data', filtered_data, ...
'electrodes', electrode_table_region, ...
'filtering', 'Band-pass filtered between 4 and 8 Hz', ...
'starting_time', 0.0, ...
'starting_time_rate', 200.0 ...
);
 
% Create a FilteredEphys object and add the filtered electrical series
filtered_ephys = types.core.FilteredEphys();
filtered_ephys.electricalseries.set('FilteredElectricalSeries', filtered_electrical_series);
 
% Add the FilteredEphys object to the ecephys module
ecephys_module.nwbdatainterface.set('FilteredEphys', filtered_ephys);

Decomposition of LFP Data into Frequency Bands

In some cases, you may want to further process the LFP data and decompose the signal into different frequency bands for additional downstream analyses. You can then store the processed data from these spectral analyses using a DecompositionSeries object. This object allows you to include metadata about the frequency bands and metric used (e.g., power, phase, amplitude), as well as link the decomposed data to the original TimeSeries signal the data was derived from.
In this tutorial, the examples for FilteredEphys and DecompositionSeries may appear similar. However, the key difference is that DecompositionSeries is specialized for storing the results of spectral analyses of timeseries data in general, whereas FilteredEphys is defined specifically as a container for filtered electrical signals.
Note: When adding data to a DecompositionSeries, the data argument is assumed to be 3D where the first dimension is time, the second dimension is channels, and the third dimension is bands. As mentioned in the beginning of this tutorial, in MatNWB the data needs to be permuted because the dimensions are written to file in reverse order (See the dimensionMapNoDataPipes tutorial)
% Define the frequency bands of interest (in Hz):
band_names = {'theta'; 'beta'; 'gamma'};
band_mean = [8; 21; 55];
band_stdev = [2; 4.5; 12.5];
band_limits = [band_mean - 2*band_stdev, band_mean + 2*band_stdev];
 
% The bands should be added to the DecompositionSeries as a dynamic table
bands = table(band_names, band_mean, band_stdev, band_limits, ...
'VariableNames', {'band_names', 'band_mean', 'band_stdev', 'band_limits'})
bands = 3×4 table
 band_namesband_meanband_stdevband_limits
12
1'theta'82412
2'beta'214.50001230
3'gamma'5512.50003080
 
bands = util.table2nwb( bands );
 
% Generate random phase data for the demonstration.
phase_data = randn(50, 12, numel(band_names)); % 50 samples, 12 channels, 3 frequency bands
phase_data = permute(phase_data, [3,2,1]); % See dimensionMapNoDataPipes tutorial
 
decomp_series = types.core.DecompositionSeries(...
'data', phase_data, ...
'bands', bands, ...
'metric', 'phase', ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000.0, ... % Hz
'source_channels', electrode_table_region, ...
'source_timeseries', lfp_electrical_series);
 
% Add decomposition series to ecephys module
ecephys_module.nwbdatainterface.set('theta', decomp_series);

Spike Times and Extracellular Events

Sorted Spike Times

Spike times are stored in a Units table, a specialization of the DynamicTable class. The default Units table is located at /units in the HDF5 file. You can add columns to the Units table just like you did for electrodes and trials (see convertTrials). Here, we generate some random spike data and populate the table.
num_cells = 10;
spikes = cell(1, num_cells);
for iShank = 1:num_cells
spikes{iShank} = rand(1, randi([16, 28]));
end
spikes
spikes = 1×10 cell
 12345678910
11×24 double1×28 double1×22 double1×28 double1×21 double1×26 double1×16 double1×18 double1×24 double1×24 double

Ragged Arrays

Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the Units table. These indexed columns have two components, the VectorData object that holds the data and the VectorIndex object that holds the indices in the vector that indicate the row breaks. You can use the convenience function util.create_indexed_column to create these objects. For more information about ragged arrays, we refer you to the "Ragged Array Columns" section of the dynamic table tutorial.
[spike_times_vector, spike_times_index] = util.create_indexed_column(spikes);
 
nwb.units = types.core.Units( ...
'colnames', {'spike_times'}, ...
'description', 'units table', ...
'spike_times', spike_times_vector, ...
'spike_times_index', spike_times_index ...
);
 
nwb.units.toTable
ans = 10×2 table
 idspike_times
1124×1 double
2228×1 double
3322×1 double
4428×1 double
5521×1 double
6626×1 double
7716×1 double
8818×1 double
9924×1 double
101024×1 double

Unsorted Spike Times

While the Units table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using SpikeEventSeries objects.
% In the SpikeEventSeries the dimensions should be ordered as
% [num_events, num_channels, num_samples].
% Define spike snippets: 20 events, 3 channels, 40 samples per event.
spike_snippets = rand(20, 3, 40);
% Permute spike snippets (See dimensionMapNoDataPipes tutorial)
spike_snippets = permute(spike_snippets, [3,2,1])
spike_snippets =
spike_snippets(:,:,1) = - - 0.7910 0.0213 0.8335 - 0.8083 0.5109 0.0694 - 0.0414 0.1315 0.9237 - 0.6840 0.0158 0.8003 - 0.9652 0.0518 0.6909 - 0.4137 0.6962 0.6477 - 0.7065 0.4276 0.1037 - 0.0327 0.2995 0.3123 - 0.5643 0.7282 0.7042 - 0.3009 0.8131 0.9222 - 0.1623 0.8546 0.4080 - 0.3810 0.7375 0.5178 - 0.1005 0.8742 0.2635 - 0.9530 0.3118 0.5444 - 0.0115 0.0973 0.5146 - 0.1436 0.2857 0.3713 - 0.5966 0.3174 0.4652 - 0.0131 0.8012 0.6369 - 0.4585 0.6484 0.6865 - 0.5535 0.1510 0.3442 - 0.0254 0.5062 0.8555 - 0.7063 0.5182 0.6404 - 0.3815 0.1169 0.1685 - 0.8255 0.1120 0.3037 - 0.1461 0.4718 0.1424 - 0.8712 0.8092 0.7453 - 0.3177 0.3974 0.9020 - 0.4894 0.6920 0.5508 - 0.1267 0.0666 0.8641 - 0.9720 0.7211 0.6973 - 0.8166 0.9120 0.1665 - 0.4227 0.3168 0.6086 - 0.0780 0.9907 0.1200 - 0.0661 0.3271 0.7940 - 0.8019 0.9782 0.7975 - 0.4981 0.6964 0.9912 - 0.5068 0.4475 0.9353 - 0.4478 0.6194 0.5117 - 0.8898 0.5428 0.7175 - 0.8606 0.2924 0.9609 +

Electrode Information

In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an electrodes table, which is also a DynamicTable. electrodes has several required fields: x, y, z, impedance, location, filtering, and electrode_group.

Electrodes Table

Since this is a DynamicTable, we can add additional metadata fields. We will be adding a "label" column to the table.
numShanks = 4;
numChannelsPerShank = 3;
numChannels = numShanks * numChannelsPerShank;
 
electrodesDynamicTable = types.hdmf_common.DynamicTable(...
'colnames', {'location', 'group', 'group_name', 'label'}, ...
'description', 'all electrodes');
 
device = types.core.Device(...
'description', 'the best array', ...
'manufacturer', 'Probe Company 9000' ...
);
nwb.general_devices.set('array', device);
for iShank = 1:numShanks
shankGroupName = sprintf('shank%d', iShank);
electrodeGroup = types.core.ElectrodeGroup( ...
'description', sprintf('electrode group for %s', shankGroupName), ...
'location', 'brain area', ...
'device', types.untyped.SoftLink(device) ...
);
nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);
for iElectrode = 1:numChannelsPerShank
electrodesDynamicTable.addRow( ...
'location', 'unknown', ...
'group', types.untyped.ObjectView(electrodeGroup), ...
'group_name', shankGroupName, ...
'label', sprintf('%s-electrode%d', shankGroupName, iElectrode));
end
end
electrodesDynamicTable.toTable() % Display the table
ans = 12×5 table
 idlocationgroupgroup_namelabel
10'unknown'1×1 ObjectView'shank1''shank1-electrode1'
21'unknown'1×1 ObjectView'shank1''shank1-electrode2'
32'unknown'1×1 ObjectView'shank1''shank1-electrode3'
43'unknown'1×1 ObjectView'shank2''shank2-electrode1'
54'unknown'1×1 ObjectView'shank2''shank2-electrode2'
65'unknown'1×1 ObjectView'shank2''shank2-electrode3'
76'unknown'1×1 ObjectView'shank3''shank3-electrode1'
87'unknown'1×1 ObjectView'shank3''shank3-electrode2'
98'unknown'1×1 ObjectView'shank3''shank3-electrode3'
109'unknown'1×1 ObjectView'shank4''shank4-electrode1'
1110'unknown'1×1 ObjectView'shank4''shank4-electrode2'
1211'unknown'1×1 ObjectView'shank4''shank4-electrode3'
nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;

Links

In the above loop, we create ElectrodeGroup objects. The electrodes table then uses an ObjectView in each row to link to the corresponding ElectrodeGroup object. An ObjectView is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.

Recorded Extracellular Signals

Voltage data are stored using the ElectricalSeries class, a subclass of the TimeSeries class specialized for voltage data.

Referencing Electrodes

In order to create our ElectricalSeries object, we first need to reference a set of rows in the electrodes table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a DynamicTableRegion, which is a type of link that allows you to reference specific rows of a DynamicTable, such as the electrodes table, using row indices.
Create a DynamicTableRegion that references all rows of the electrodes table.
electrode_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'all electrodes', ...
'data', (0:length(electrodesDynamicTable.id.data)-1)');

Raw Voltage Data

Now create an ElectricalSeries object to hold acquisition data collected during the experiment.
raw_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 30000., ... % Hz
'data', randn(numChannels, 3000), ... % nChannels x nTime
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.
nwb.acquisition.set('ElectricalSeries', raw_electrical_series);

Processed Extracellular Electrical Signals

LFP

LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an ElectricalSeries. To help data analysis and visualization tools know that this ElectricalSeries object represents LFP data, we store it inside an LFP object and then place the LFP object in a ProcessingModule named 'ecephys'. This is analogous to how we stored the SpatialSeries object inside of a Position object and stored the Position object in a ProcessingModule named 'behavior' in the behavior tutorial
lfp_electrical_series = types.core.ElectricalSeries( ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000., ... % Hz
'data', randn(numChannels, 100), ... nChannels x nTime
'filtering', 'Low-pass filter at 300 Hz', ...
'electrodes', electrode_table_region, ...
'data_unit', 'volts');
 
lfp = types.core.LFP('ElectricalSeries', lfp_electrical_series);
 
ecephys_module = types.core.ProcessingModule(...
'description', 'extracellular electrophysiology');
 
ecephys_module.nwbdatainterface.set('LFP', lfp);
nwb.processing.set('ecephys', ecephys_module);

Other Types of Filtered Electrical Signals

If your acquired data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you can store the result in an ElectricalSeries and encapsulate it within a FilteredEphys object instead of the LFP object.
% Generate filtered data
filtered_data = randn(50, 12); % 50 time points, 12 channels
filtered_data = permute(filtered_data, [2, 1]); % permute timeseries for matnwb
 
% Create an ElectricalSeries object
filtered_electrical_series = types.core.ElectricalSeries( ...
'description', 'Data filtered in the theta range', ...
'data', filtered_data, ...
'electrodes', electrode_table_region, ...
'filtering', 'Band-pass filtered between 4 and 8 Hz', ...
'starting_time', 0.0, ...
'starting_time_rate', 200.0 ...
);
 
% Create a FilteredEphys object and add the filtered electrical series
filtered_ephys = types.core.FilteredEphys();
filtered_ephys.electricalseries.set('FilteredElectricalSeries', filtered_electrical_series);
 
% Add the FilteredEphys object to the ecephys module
ecephys_module.nwbdatainterface.set('FilteredEphys', filtered_ephys);

Decomposition of LFP Data into Frequency Bands

In some cases, you may want to further process the LFP data and decompose the signal into different frequency bands for additional downstream analyses. You can then store the processed data from these spectral analyses using a DecompositionSeries object. This object allows you to include metadata about the frequency bands and metric used (e.g., power, phase, amplitude), as well as link the decomposed data to the original TimeSeries signal the data was derived from.
In this tutorial, the examples for FilteredEphys and DecompositionSeries may appear similar. However, the key difference is that DecompositionSeries is specialized for storing the results of spectral analyses of timeseries data in general, whereas FilteredEphys is defined specifically as a container for filtered electrical signals.
Note: When adding data to a DecompositionSeries, the data argument is assumed to be 3D where the first dimension is time, the second dimension is channels, and the third dimension is bands. As mentioned in the beginning of this tutorial, in MatNWB the data needs to be permuted because the dimensions are written to file in reverse order (See the dimensionMapNoDataPipes tutorial)
% Define the frequency bands of interest (in Hz):
band_names = {'theta'; 'beta'; 'gamma'};
band_mean = [8; 21; 55];
band_stdev = [2; 4.5; 12.5];
band_limits = [band_mean - 2*band_stdev, band_mean + 2*band_stdev];
 
% The bands should be added to the DecompositionSeries as a dynamic table
bands = table(band_names, band_mean, band_stdev, band_limits, ...
'VariableNames', {'band_name', 'band_mean', 'band_stdev', 'band_limits'})
bands = 3×4 table
 band_nameband_meanband_stdevband_limits
12
1'theta'82412
2'beta'214.50001230
3'gamma'5512.50003080
 
bands = util.table2nwb( bands );
 
% Generate random phase data for the demonstration.
phase_data = randn(50, 12, numel(band_names)); % 50 samples, 12 channels, 3 frequency bands
phase_data = permute(phase_data, [3,2,1]); % See dimensionMapNoDataPipes tutorial
 
decomp_series = types.core.DecompositionSeries(...
'data', phase_data, ...
'bands', bands, ...
'metric', 'phase', ...
'starting_time', 0.0, ... % seconds
'starting_time_rate', 1000.0, ... % Hz
'source_channels', electrode_table_region, ...
'source_timeseries', lfp_electrical_series);
 
% Add decomposition series to ecephys module
ecephys_module.nwbdatainterface.set('theta', decomp_series);

Spike Times and Extracellular Events

Sorted Spike Times

Spike times are stored in a Units table, a specialization of the DynamicTable class. The default Units table is located at /units in the HDF5 file. You can add columns to the Units table just like you did for electrodes and trials (see convertTrials). Here, we generate some random spike data and populate the table.
num_cells = 10;
spikes = cell(1, num_cells);
for iShank = 1:num_cells
spikes{iShank} = rand(1, randi([16, 28]));
end
spikes
spikes = 1×10 cell
 12345678910
11×24 double1×19 double1×20 double1×25 double1×22 double1×20 double1×22 double1×17 double1×21 double1×25 double

Ragged Arrays

Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the Units table. These indexed columns have two components, the VectorData object that holds the data and the VectorIndex object that holds the indices in the vector that indicate the row breaks. You can use the convenience function util.create_indexed_column to create these objects. For more information about ragged arrays, we refer you to the "Ragged Array Columns" section of the dynamic table tutorial.
[spike_times_vector, spike_times_index] = util.create_indexed_column(spikes);
 
nwb.units = types.core.Units( ...
'colnames', {'spike_times'}, ...
'description', 'units table', ...
'spike_times', spike_times_vector, ...
'spike_times_index', spike_times_index ...
);
 
nwb.units.toTable
ans = 10×2 table
 idspike_times
1124×1 double
2219×1 double
3320×1 double
4425×1 double
5522×1 double
6620×1 double
7722×1 double
8817×1 double
9921×1 double
101025×1 double

Unsorted Spike Times

While the Units table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using SpikeEventSeries objects.
% In the SpikeEventSeries the dimensions should be ordered as
% [num_events, num_channels, num_samples].
% Define spike snippets: 20 events, 3 channels, 40 samples per event.
spike_snippets = rand(20, 3, 40);
% Permute spike snippets (See dimensionMapNoDataPipes tutorial)
spike_snippets = permute(spike_snippets, [3,2,1])
spike_snippets =
spike_snippets(:,:,1) = + + 0.3732 0.7791 0.5937 + 0.7586 0.8887 0.6501 + 0.3051 0.4772 0.4288 + 0.4012 0.5913 0.3957 + 0.3939 0.3286 0.1415 + 0.5662 0.5443 0.2179 + 0.9869 0.4951 0.4386 + 0.7861 0.9518 0.4311 + 0.6751 0.4070 0.1629 + 0.4426 0.5435 0.8580 + 0.0396 0.6455 0.9119 + 0.9309 0.4391 0.6281 + 0.2086 0.5926 0.6285 + 0.9404 0.2170 0.2241 + 0.5310 0.3074 0.9547 + 0.3391 0.8501 0.5889 + 0.0462 0.7716 0.8651 + 0.0262 0.6235 0.9419 + 0.8849 0.0555 0.4975 + 0.4348 0.8429 0.5563 + 0.3528 0.4937 0.8508 + 0.5342 0.9424 0.9996 + 0.5746 0.0628 0.8755 + 0.4744 0.8553 0.9244 + 0.7230 0.6936 0.2125 + 0.4087 0.2393 0.7200 + 0.2982 0.4357 0.2804 + 0.1524 0.7107 0.3372 + 0.9375 0.9103 0.1958 + 0.7425 0.0687 0.4879 + 0.2087 0.0370 0.0393 + 0.1920 0.2053 0.4417 + 0.1422 0.4232 0.1870 + 0.8810 0.7692 0.7563 + 0.1456 0.9029 0.5702 + 0.2523 0.1514 0.7324 + 0.4195 0.4896 0.2667 + 0.3307 0.6090 0.6230 + 0.1701 0.9241 0.5880 + 0.5319 0.0420 0.6809 spike_snippets(:,:,2) = - 0.5338 0.3753 0.6193 - 0.1517 0.6080 0.1663 - 0.1934 0.6342 0.9882 - 0.9244 0.8932 0.3000 - 0.8507 0.4194 0.6482 - 0.5106 0.1159 0.7561 - 0.1162 0.3095 0.9177 - 0.9806 0.6443 0.8760 - 0.0513 0.4850 0.9481 - 0.8886 0.2191 0.6419 - 0.6883 0.4286 0.8974 - 0.8499 0.5069 0.4643 - 0.9019 0.3152 0.4006 - 0.2973 0.4501 0.0411 - 0.2026 0.1939 0.9824 - 0.6041 0.2842 0.5462 - 0.9371 0.5834 0.8635 - 0.4799 0.6260 0.2953 - 0.5238 0.7532 0.8461 - 0.6517 0.7502 0.3512 - 0.4229 0.1752 0.1634 - 0.7366 0.7801 0.9180 - 0.1957 0.2408 0.3131 - 0.9544 0.5748 0.1483 - 0.8544 0.0117 0.7080 - 0.7467 0.9576 0.2643 - 0.5438 0.0537 0.0843 - 0.9563 0.6243 0.4454 - 0.5092 0.1294 0.3496 - 0.4297 0.4393 0.2485 - 0.4890 0.8836 0.6625 - 0.1389 0.4318 0.3081 - 0.1869 0.0011 0.4418 - 0.9201 0.4152 0.0008 - 0.4235 0.6870 0.1545 - 0.9545 0.0976 0.9682 - 0.7207 0.6316 0.7971 - 0.9008 0.5584 0.7311 - 0.1453 0.9232 0.3198 - 0.5304 0.4056 0.1549 + 0.6946 0.9867 0.1621 + 0.2072 0.7996 0.6578 + 0.7396 0.9594 0.2076 + 0.5369 0.8403 0.3706 + 0.3963 0.8120 0.9353 + 0.0532 0.0624 0.7060 + 0.1420 0.1284 0.4933 + 0.9286 0.5055 0.6835 + 0.3156 0.7733 0.3820 + 0.4400 0.2803 0.6084 + 0.2611 0.6750 0.0324 + 0.0603 0.6302 0.9327 + 0.3091 0.1783 0.3724 + 0.5451 0.6656 0.4110 + 0.1974 0.1752 0.6761 + 0.9598 0.3204 0.7998 + 0.7463 0.9291 0.1296 + 0.1828 0.4812 0.6270 + 0.7885 0.1908 0.7519 + 0.1154 0.7749 0.6845 + 0.5216 0.3994 0.9655 + 0.2219 0.3672 0.2583 + 0.4704 0.8092 0.4067 + 0.9942 0.0751 0.1858 + 0.2373 0.5807 0.9973 + 0.6321 0.7136 0.1707 + 0.4648 0.7071 0.1290 + 0.6359 0.6416 0.3091 + 0.1669 0.8553 0.1380 + 0.7639 0.3221 0.2149 + 0.0145 0.3226 0.4020 + 0.5560 0.0633 0.2573 + 0.6934 0.7846 0.9890 + 0.5472 0.7723 0.0297 + 0.7413 0.4124 0.4747 + 0.9513 0.8615 0.3798 + 0.8774 0.0074 0.0822 + 0.2707 0.2008 0.6769 + 0.9066 0.2487 0.3473 + 0.9031 0.5947 0.2998 spike_snippets(:,:,3) = - 0.5092 0.9115 0.6848 - 0.0370 0.3598 0.5459 - 0.8909 0.2320 0.6210 - 0.1415 0.3598 0.3532 - 0.6650 0.7777 0.2036 - 0.0845 0.7893 0.6989 - 0.8263 0.5907 0.7732 - 0.7758 0.9209 0.6862 - 0.6141 0.4780 0.6086 - 0.4845 0.5522 0.5599 - 0.9572 0.1676 0.3414 - 0.1539 0.5025 0.8494 - 0.6809 0.5092 0.8636 - 0.9649 0.6492 0.5732 - 0.3245 0.6510 0.9502 - 0.3759 0.3767 0.4420 - 0.7450 0.5653 0.0341 - 0.8066 0.5285 0.6691 - 0.7006 0.0285 0.2575 - 0.4817 0.5484 0.8613 - 0.2185 0.3218 0.5705 - 0.4282 0.1795 0.8811 - 0.3090 0.8398 0.6193 - 0.0477 0.4053 0.8866 - 0.1156 0.5851 0.2542 - 0.8462 0.0158 0.3698 - 0.4941 0.9994 0.2657 - 0.6635 0.6725 0.1791 - 0.8944 0.2112 0.0403 - 0.3573 0.8075 0.2363 - 0.0931 0.0854 0.2321 - 0.4501 0.9337 0.2882 - 0.5619 0.6824 0.8621 - 0.5455 0.0936 0.0346 - 0.4649 0.7467 0.8067 - 0.0441 0.9981 0.1671 - 0.1436 0.4552 0.5099 - 0.9111 0.4916 0.9271 - 0.1548 0.1222 0.4906 - 0.3945 0.8146 0.5500 + 0.5451 0.8070 0.3206 + 0.6928 0.4280 0.8321 + 0.6805 0.9535 0.2361 + 0.2666 0.1868 0.4845 + 0.9815 0.7233 0.8900 + 0.7260 0.8210 0.3314 + 0.3164 0.7107 0.6713 + 0.4181 0.4670 0.0010 + 0.9741 0.2856 0.7074 + 0.4238 0.8375 0.8412 + 0.9819 0.8191 0.0949 + 0.4364 0.9326 0.4354 + 0.7310 0.9098 0.4869 + 0.4943 0.9199 0.3313 + 0.6452 0.6932 0.7494 + 0.0505 0.9546 0.9591 + 0.1264 0.4607 0.1279 + 0.9333 0.0234 0.7867 + 0.3341 0.3857 0.6957 + 0.7642 0.1095 0.0429 + 0.9580 0.4884 0.6948 + 0.6084 0.5523 0.8079 + 0.5066 0.6660 0.3920 + 0.0327 0.7744 0.8886 + 0.9240 0.5647 0.0658 + 0.5356 0.0641 0.7811 + 0.4054 0.5640 0.4154 + 0.9789 0.5963 0.8608 + 0.5668 0.1509 0.3670 + 0.7025 0.9988 0.9933 + 0.8613 0.4405 0.1464 + 0.1041 0.7715 0.3392 + 0.3661 0.3882 0.5087 + 0.7465 0.0436 0.0087 + 0.3572 0.4768 0.0233 + 0.9081 0.2854 0.6945 + 0.2392 0.3414 0.6361 + 0.6355 0.0123 0.6029 + 0.8906 0.9561 0.0998 + 0.8724 0.4231 0.8791 spike_snippets(:,:,4) = - 0.8651 0.0729 0.5703 - 0.7056 0.7361 0.0422 - 0.7220 0.4867 0.8848 - 0.7563 0.2299 0.4345 - 0.1388 0.6244 0.1856 - 0.4175 0.1199 0.3165 - 0.0970 0.6949 0.4674 - 0.6304 0.8521 0.2420 - 0.5424 0.2486 0.6067 - 0.1731 0.3253 0.6955 - 0.6922 0.3908 0.0253 - 0.3011 0.2044 0.2150 - 0.6281 0.9983 0.8887 - 0.5463 0.3738 0.2357 - 0.1263 0.2581 0.0286 - 0.5020 0.1082 0.0744 - 0.2669 0.0954 0.5680 - 0.3439 0.2201 0.8495 - 0.6176 0.9906 0.6916 - 0.2697 0.4133 0.1197 - 0.5944 0.7802 0.2287 - 0.4447 0.7085 0.0679 - 0.1947 0.6754 0.9432 - 0.4714 0.0661 0.0817 - 0.5271 0.9569 0.9435 - 0.8597 0.6655 0.0351 - 0.8275 0.0531 0.2852 - 0.2070 0.3484 0.4845 - 0.9358 0.0522 0.7680 - 0.6787 0.3460 0.3718 - 0.2934 0.3135 0.0939 - 0.9152 0.4345 0.1295 - 0.3650 0.2472 0.3402 - 0.1528 0.6372 0.5485 - 0.5785 0.9504 0.9111 - 0.5069 0.9808 0.3079 - 0.3256 0.3353 0.1383 - 0.5329 0.4452 0.8509 - 0.1127 0.6844 0.8668 - 0.7811 0.1499 0.4629 + 0.4854 0.2021 0.4178 + 0.1042 0.0572 0.5235 + 0.7787 0.8715 0.3109 + 0.0947 0.2513 0.3742 + 0.4724 0.0945 0.4348 + 0.7312 0.1811 0.7789 + 0.1908 0.4436 0.7879 + 0.3579 0.8552 0.1141 + 0.4405 0.4878 0.4577 + 0.7250 0.3002 0.0478 + 0.4961 0.2143 0.2460 + 0.3935 0.5856 0.3734 + 0.4304 0.8539 0.6590 + 0.4677 0.2888 0.9005 + 0.6995 0.0156 0.3576 + 0.8153 0.7794 0.9313 + 0.6333 0.6757 0.7350 + 0.5318 0.6517 0.2361 + 0.8009 0.7342 0.3832 + 0.2961 0.9837 0.3576 + 0.2021 0.9876 0.7297 + 0.9424 0.2764 0.0504 + 0.1479 0.1211 0.1844 + 0.4527 0.4286 0.2950 + 0.4787 0.1957 0.8230 + 0.4661 0.1299 0.0478 + 0.4762 0.0754 0.6567 + 0.4106 0.8626 0.7855 + 0.2155 0.7685 0.4191 + 0.8953 0.0073 0.6560 + 0.2029 0.2695 0.3976 + 0.1809 0.4246 0.5822 + 0.9363 0.5071 0.9302 + 0.3073 0.1328 0.5443 + 0.6552 0.0491 0.8480 + 0.3227 0.3559 0.1201 + 0.0361 0.0332 0.5380 + 0.0322 0.5357 0.4574 + 0.8864 0.4393 0.4757 + 0.3848 0.2086 0.7510 spike_snippets(:,:,5) = - 0.9833 0.1306 0.1439 - 0.2656 0.5451 0.9737 - 0.5678 0.3446 0.3483 - 0.0967 0.0733 0.2677 - 0.2653 0.9260 0.8876 - 0.3652 0.3403 0.3428 - 0.7242 0.2202 0.3057 - 0.8023 0.8464 0.9116 - 0.2288 0.7517 0.9695 - 0.7929 0.6289 0.9108 - 0.5254 0.5371 0.8497 - 0.9644 0.9208 0.5796 - 0.3907 0.0955 0.7351 - 0.8862 0.8287 0.5880 - 0.3508 0.8420 0.7759 - 0.9350 0.8327 0.3354 - 0.3336 0.4643 0.3187 - 0.3489 0.0708 0.8605 - 0.6006 0.7239 0.3991 - 0.3062 0.9719 0.1192 - 0.6141 0.9838 0.1967 - 0.0040 0.5215 0.8946 - 0.2364 0.0775 0.0065 - 0.7518 0.9408 0.8491 - 0.7013 0.8655 0.8282 - 0.9326 0.1311 0.8476 - 0.1252 0.0149 0.3416 - 0.9947 0.6125 0.6894 - 0.1763 0.6221 0.2775 - 0.7586 0.2494 0.6819 - 0.9048 0.7648 0.0505 - 0.2789 0.3761 0.7825 - 0.1195 0.3080 0.1136 - 0.3286 0.8026 0.9635 - 0.5285 0.2380 0.6579 - 0.4777 0.2527 0.4029 - 0.6137 0.4510 0.4112 - 0.3054 0.5817 0.7277 - 0.5103 0.9052 0.9271 - 0.8229 0.1610 0.4064 + 0.1860 0.0536 0.5940 + 0.2376 0.8684 0.8130 + 0.1716 0.3393 0.4770 + 0.0312 0.1379 0.6187 + 0.3256 0.6297 0.1215 + 0.8935 0.3748 0.4845 + 0.6420 0.1018 0.4572 + 0.1572 0.6465 0.1285 + 0.7944 0.8081 0.1087 + 0.3353 0.1870 0.9674 + 0.3222 0.3932 0.3531 + 0.8497 0.5727 0.3463 + 0.6986 0.6426 0.5585 + 0.7362 0.5711 0.3389 + 0.1066 0.8356 0.3358 + 0.9519 0.5272 0.1312 + 0.0989 0.9641 0.3374 + 0.5441 0.7474 0.6270 + 0.9346 0.6895 0.6218 + 0.7645 0.7542 0.8233 + 0.0604 0.9699 0.0417 + 0.1195 0.0637 0.8807 + 0.4555 0.7875 0.7380 + 0.3830 0.6953 0.0521 + 0.0568 0.6954 0.6580 + 0.3506 0.4798 0.0857 + 0.2994 0.7058 0.4222 + 0.3651 0.4766 0.6714 + 0.6819 0.1449 0.8841 + 0.7518 0.6569 0.8799 + 0.4554 0.0071 0.1870 + 0.1671 0.5957 0.8323 + 0.7819 0.1208 0.0767 + 0.5283 0.8852 0.1754 + 0.4018 0.9644 0.2548 + 0.3075 0.7336 0.8771 + 0.8817 0.0490 0.6314 + 0.9053 0.7864 0.7232 + 0.3306 0.1048 0.3259 + 0.4217 0.0060 0.3249 spike_snippets(:,:,6) = - 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0.2248 0.9454 0.3343 - 0.2661 0.2714 0.5529 - 0.6409 0.5257 0.2509 - 0.9720 0.6149 0.1317 - 0.4660 0.5646 0.0069 - 0.6395 0.8293 0.7489 - 0.0363 0.0109 0.0759 - 0.1006 0.2261 0.0331 - 0.4483 0.7455 0.8873 - 0.4724 0.9053 0.7795 - 0.6163 0.1059 0.4694 - 0.5038 0.8689 0.7092 - 0.4000 0.2480 0.9829 - 0.2566 0.8716 0.0221 - 0.1963 0.2623 0.1946 - 0.8754 0.8960 0.0683 - 0.3214 0.5532 0.3871 - 0.3090 0.4612 0.3692 - 0.0949 0.9036 0.7243 - 0.5785 0.0853 0.4553 - 0.4080 0.4273 0.1826 - 0.2397 0.1025 0.2676 - 0.0112 0.4233 0.8566 - 0.6912 0.5644 0.1537 - 0.5037 0.1292 0.5835 - 0.8740 0.9701 0.2860 - 0.1776 0.8694 0.4239 - 0.0146 0.2931 0.1575 - 0.1438 0.2192 0.0534 - 0.1210 0.6226 0.3310 - 0.6808 0.5873 0.8866 - 0.0166 0.4711 0.0265 - 0.2377 0.0932 0.4810 - 0.4095 0.5646 0.3677 - 0.7206 0.4269 0.0106 - 0.4908 0.5084 0.7710 - 0.9168 0.9261 0.2838 - 0.0650 0.3080 0.7490 - 0.9420 0.4491 0.0547 - 0.6323 0.8976 0.8351 + 0.1959 0.5420 0.9105 + 0.9073 0.6598 0.9800 + 0.3511 0.7097 0.4335 + 0.6976 0.0432 0.8542 + 0.4499 0.8647 0.0570 + 0.6184 0.1866 0.5088 + 0.1193 0.2679 0.1645 + 0.6003 0.3598 0.0318 + 0.7014 0.7784 0.4366 + 0.2962 0.9476 0.4504 + 0.3308 0.3297 0.9279 + 0.2546 0.0680 0.8303 + 0.9917 0.5504 0.9679 + 0.8936 0.4322 0.1293 + 0.5590 0.5348 0.5241 + 0.4043 0.1695 0.9367 + 0.2596 0.0089 0.7440 + 0.3231 0.3334 0.0031 + 0.2933 0.8405 0.0099 + 0.3029 0.3807 0.3762 + 0.3166 0.8088 0.7310 + 0.4989 0.6476 0.8311 + 0.1168 0.6515 0.7213 + 0.2899 0.3840 0.6481 + 0.1497 0.6184 0.0411 + 0.0861 0.9320 0.1645 + 0.3731 0.4888 0.1075 + 0.3739 0.0245 0.9520 + 0.2799 0.4943 0.5538 + 0.2278 0.6777 0.3999 + 0.2515 0.1443 0.8072 + 0.5196 0.6802 0.8547 + 0.9614 0.4804 0.2351 + 0.3108 0.2935 0.7144 + 0.6170 0.7830 0.1604 + 0.3423 0.2268 0.9950 + 0.0840 0.1316 0.4855 + 0.9375 0.8720 0.7160 + 0.8194 0.4043 0.6775 + 0.2686 0.9371 0.0693 spike_snippets(:,:,18) = - 0.8528 0.8267 0.3742 - 0.7533 0.1331 0.9552 - 0.4917 0.0555 0.7208 - 0.8872 0.2577 0.6193 - 0.5457 0.5930 0.0307 - 0.1417 0.9495 0.2944 - 0.3786 0.4841 0.4944 - 0.8947 0.1513 0.0882 - 0.5805 0.2407 0.3165 - 0.4782 0.4752 0.6826 - 0.3881 0.8809 0.0212 - 0.1409 0.6620 0.2262 - 0.7749 0.3939 0.4166 - 0.7476 0.2595 0.6855 - 0.8465 0.2595 0.1188 - 0.5590 0.4504 0.5791 - 0.8377 0.9104 0.6629 - 0.6318 0.3158 0.3771 - 0.8022 0.9276 0.2942 - 0.2505 0.8024 0.0318 - 0.9150 0.5174 0.6685 - 0.0144 0.9595 0.8606 - 0.4961 0.8575 0.2233 - 0.5635 0.4358 0.9664 - 0.3172 0.9393 0.1256 - 0.1253 0.8188 0.1029 - 0.3440 0.3788 0.0650 - 0.7378 0.9603 0.1648 - 0.4810 0.1027 0.5597 - 0.0185 0.2453 0.5999 - 0.8983 0.2843 0.7902 - 0.9571 0.6531 0.9934 - 0.3729 0.3625 0.7644 - 0.8940 0.6779 0.7209 - 0.0921 0.6594 0.9088 - 0.1161 0.6699 0.5187 - 0.6088 0.3770 0.6351 - 0.6902 0.4612 0.4053 - 0.1904 0.8284 0.8358 - 0.0138 0.0373 0.1868 + 0.2044 0.8060 0.4559 + 0.3989 0.3477 0.0178 + 0.3788 0.1528 0.2253 + 0.4920 0.6866 0.6046 + 0.0173 0.5004 0.6817 + 0.4821 0.8199 0.8309 + 0.2736 0.1016 0.2971 + 0.9217 0.5541 0.4229 + 0.1621 0.0677 0.9017 + 0.5945 0.1912 0.6291 + 0.2916 0.5514 0.5131 + 0.2112 0.8000 0.3688 + 0.8284 0.4171 0.1074 + 0.8941 0.1970 0.8132 + 0.5454 0.1708 0.5964 + 0.0349 0.1684 0.5245 + 0.5870 0.6541 0.5613 + 0.7540 0.6592 0.1215 + 0.1668 0.6609 0.2367 + 0.6786 0.1693 0.0634 + 0.9586 0.3644 0.4699 + 0.3425 0.2172 0.2802 + 0.1701 0.9606 0.7301 + 0.9152 0.3249 0.7825 + 0.8650 0.4647 0.2678 + 0.7262 0.6912 0.9897 + 0.4532 0.6555 0.2157 + 0.7250 0.0509 0.3455 + 0.3804 0.2556 0.8391 + 0.6323 0.8273 0.0621 + 0.7440 0.9607 0.9505 + 0.5396 0.6674 0.5746 + 0.5364 0.2100 0.9133 + 0.5956 0.7204 0.8166 + 0.4304 0.9428 0.1365 + 0.2596 0.5669 0.2789 + 0.1343 0.4623 0.9535 + 0.2614 0.6911 0.2445 + 0.4018 0.0246 0.3004 + 0.0077 0.1523 0.3515 spike_snippets(:,:,19) = - 0.7833 0.3800 0.2596 - 0.4774 0.3225 0.9132 - 0.3767 0.5766 0.7866 - 0.5167 0.7333 0.2876 - 0.3313 0.6348 0.4921 - 0.9909 0.7142 0.0040 - 0.7992 0.8899 0.7669 - 0.1438 0.3450 0.1557 - 0.8615 0.5972 0.6849 - 0.2221 0.2920 0.8230 - 0.9428 0.8654 0.7775 - 0.5121 0.7407 0.5810 - 0.6163 0.4000 0.3330 - 0.3748 0.9231 0.6470 - 0.4877 0.7523 0.8188 - 0.4864 0.9930 0.0859 - 0.0486 0.4711 0.6532 - 0.3978 0.0564 0.0195 - 0.9749 0.1345 0.4348 - 0.4293 0.0039 0.9382 - 0.5667 0.6412 0.2270 - 0.5779 0.7878 0.6903 - 0.4924 0.9922 0.8492 - 0.6379 0.1296 0.5580 - 0.4197 0.5457 0.1361 - 0.4808 0.5291 0.6389 - 0.6873 0.0123 0.3525 - 0.9886 0.1202 0.7538 - 0.7048 0.8567 0.9505 - 0.3475 0.1045 0.1739 - 0.8209 0.6846 0.5388 - 0.0525 0.3191 0.7253 - 0.1306 0.5241 0.2333 - 0.6714 0.2183 0.1283 - 0.8935 0.1168 0.5805 - 0.8253 0.1419 0.3755 - 0.6320 0.2447 0.7466 - 0.0071 0.0700 0.8273 - 0.3107 0.5529 0.8195 - 0.2268 0.3851 0.4185 + 0.4796 0.1469 0.7166 + 0.7013 0.1920 0.6477 + 0.3507 0.7289 0.3165 + 0.8040 0.2512 0.8675 + 0.8645 0.4048 0.6909 + 0.6119 0.1603 0.0853 + 0.3848 0.3168 0.8677 + 0.7272 0.9591 0.4262 + 0.4190 0.5329 0.7629 + 0.8615 0.3420 0.3994 + 0.6660 0.7825 0.5770 + 0.0337 0.5107 0.3806 + 0.2774 0.0544 0.5483 + 0.3664 0.7206 0.4609 + 0.3950 0.5821 0.2499 + 0.8709 0.9255 0.2459 + 0.4484 0.3779 0.7865 + 0.5256 0.7493 0.3957 + 0.3331 0.4078 0.6649 + 0.0315 0.3368 0.5578 + 0.8973 0.6093 0.0103 + 0.8446 0.0363 0.2517 + 0.8092 0.1453 0.8943 + 0.7750 0.4485 0.2392 + 0.8051 0.5641 0.9237 + 0.5442 0.3667 0.5998 + 0.7352 0.7777 0.9312 + 0.4753 0.1060 0.7647 + 0.6985 0.3146 0.1770 + 0.9186 0.0215 0.0437 + 0.8929 0.5599 0.3737 + 0.6774 0.9312 0.5492 + 0.2597 0.3554 0.3777 + 0.3778 0.6063 0.0516 + 0.3313 0.3930 0.4723 + 0.3727 0.7249 0.9840 + 0.7936 0.9136 0.4857 + 0.4638 0.5821 0.0701 + 0.8432 0.2344 0.4754 + 0.5643 0.0735 0.2353 spike_snippets(:,:,20) = - 0.0803 0.5497 0.8134 - 0.2016 0.7139 0.9475 - 0.2989 0.6889 0.9147 - 0.4506 0.2072 0.5099 - 0.6934 0.8924 0.4041 - 0.9842 0.2183 0.5916 - 0.9099 0.1014 0.2588 - 0.3646 0.2869 0.1107 - 0.7892 0.5949 0.2686 - 0.8226 0.5505 0.3111 - 0.0764 0.4849 0.1924 - 0.2240 0.8471 0.8423 - 0.1020 0.2254 0.4194 - 0.8691 0.5801 0.1725 - 0.8217 0.0235 0.0745 - 0.6285 0.1380 0.2667 - 0.6437 0.4091 0.5609 - 0.6716 0.2623 0.3741 - 0.7291 0.8411 0.0698 - 0.4717 0.0125 0.4050 - 0.1205 0.4536 0.2174 - 0.1712 0.4339 0.4282 - 0.2948 0.1448 0.0394 - 0.8869 0.1695 0.8341 - 0.0986 0.4590 0.6105 - 0.2961 0.1109 0.6225 - 0.2111 0.6054 0.6792 - 0.9609 0.6630 0.4239 - 0.8231 0.0917 0.1793 - 0.9510 0.1655 0.7910 - 0.8841 0.3304 0.3527 - 0.7403 0.3570 0.1069 - 0.6682 0.3612 0.9556 - 0.7742 0.0715 0.6476 - 0.8029 0.4781 0.8936 - 0.8517 0.2079 0.6216 - 0.8403 0.8003 0.7215 - 0.5677 0.7792 0.8469 - 0.1805 0.0260 0.8686 - 0.4809 0.8847 0.0151 -
 
% Create electrode table region referencing electrodes 0, 1, and 2
shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'shank0', ...
'data', (0:2)');
 
% Define spike event series for unsorted spike times
spike_events = types.core.SpikeEventSeries( ...
'data', spike_snippets, ...
'timestamps', (0:19)', ... % Timestamps for each event
'description', 'events detected with 100uV threshold', ...
'electrodes', shank0_table_region ...
);
 
% Add spike event series to NWB file acquisition
nwb.acquisition.set('SpikeEvents_Shank0', spike_events);

Detected Events

If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in ElectricalSeries objects in the acquisition group, and use the EventDetection class to identify the spike events in your raw traces.
% Create the EventDetection object
event_detection = types.core.EventDetection( ...
'detection_method', 'thresholding, 1.5 * std', ...
'source_electricalseries', types.untyped.SoftLink(raw_electrical_series), ...
'source_idx', [1000; 2000; 3000], ...
'times', [.033, .066, .099] ...
);
 
% Add the EventDetection object to the ecephys module
ecephys_module.nwbdatainterface.set('ThresholdEvents', event_detection);

Storing Spike Features (e.g Principal Components)

NWB also provides a way to store features of spikes, such as principal components, using the FeatureExtraction class.
% Generate random feature data (time x channel x feature)
features = rand(3, 12, 4); % 3 time points, 12 channels, 4 features
features = permute(features, [3,2,1]); % reverse dimension order for matnwb
 
% Create the FeatureExtraction object
feature_extraction = types.core.FeatureExtraction( ...
'description', {'PC1', 'PC2', 'PC3', 'PC4'}, ... % Feature descriptions
'electrodes', electrode_table_region, ... % DynamicTableRegion referencing the electrodes table
'times', [.033; .066; .099], ... % Column vector for times
'features', features ...
);
 
% Add the FeatureExtraction object to the ecephys module (if required)
ecephys_module.nwbdatainterface.set('PCA_features', feature_extraction);

Choosing NWB-Types for Electrophysiology Data (A Summary)

As mentioned above, ElectricalSeries objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the ElectricalSeries class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the API documentation and the section on Extracellular Physiology in the "NWB Format Specification" for more details on using these objects.
For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with ElectricalSeries objects as acquisition data, and use the EventDetection class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use SpikeEventSeries objects.
The results of spike sorting (or clustering) should be stored in the top-level Units table. The Units table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns waveform_mean, waveform_sd, and waveforms in the Units table to store individual and mean waveform data.

Writing the NWB File

nwbExport(nwb, 'ecephys_tutorial.nwb')

Reading NWB Data

Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. load with no input arguments reads the entire dataset:
nwb2 = nwbRead('ecephys_tutorial.nwb', 'ignorecache');
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data.load;

Accessing Data Regions

If all you need is a data region, you can index a DataStub object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.
% read section of LFP
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data(1:5, 1:10)
ans = 5×10
2.0421 -1.9417 0.3559 0.4354 0.6993 -1.4009 0.5222 0.0893 0.1243 1.2460 - 0.2329 0.4688 2.1159 1.2094 0.1735 -0.3315 0.1403 -2.0881 0.2840 -0.4077 - -0.0943 1.3933 -0.0871 -0.5193 -0.0920 -1.1307 0.8399 0.0975 -0.6912 -0.4536 - -1.4224 2.7602 1.1832 0.0075 0.6687 0.2074 0.5432 0.4366 0.0113 0.2925 - 0.9660 0.9444 -0.8471 1.0362 0.0652 -0.2155 0.6006 0.1602 0.7417 -1.3644 -
 
% You can use the getRow method of the table to load spike times of a specific unit.
% To get the values, unpack from the returned table.
nwb.units.getRow(1).spike_times{1}
ans = 24×1
0.1312 - 0.4211 - 0.2037 - 0.1021 - 0.1315 - 0.8392 - 0.9195 - 0.7023 - 0.0588 - 0.7331 + 0.6873 0.0605 0.9140 + 0.9657 0.0721 0.8518 + 0.8899 0.1328 0.6940 + 0.0387 0.6798 0.9621 + 0.4656 0.5908 0.2731 + 0.7438 0.5670 0.2736 + 0.3971 0.0360 0.3144 + 0.8050 0.7448 0.6957 + 0.0030 0.4603 0.2473 + 0.2465 0.6805 0.3513 + 0.8086 0.2980 0.2490 + 0.2352 0.3977 0.5605 + 0.2713 0.7731 0.5679 + 0.8310 0.7215 0.5309 + 0.7490 0.9698 0.8578 + 0.5483 0.1271 0.5879 + 0.3070 0.0921 0.7794 + 0.1199 0.8039 0.5579 + 0.9437 0.8100 0.8285 + 0.7219 0.4346 0.4720 + 0.7465 0.6658 0.1583 + 0.5633 0.8219 0.5985 + 0.3870 0.4728 0.3190 + 0.4641 0.8891 0.5325 + 0.8391 0.2668 0.5287 + 0.2010 0.5825 0.9167 + 0.9513 0.0738 0.1681 + 0.2306 0.2030 0.1066 + 0.9499 0.1480 0.6067 + 0.9601 0.6199 0.4368 + 0.9094 0.2456 0.3924 + 0.9030 0.7869 0.9830 + 0.1268 0.3279 0.8036 + 0.4003 0.7065 0.6491 + 0.6768 0.2883 0.4791 + 0.1409 0.1552 0.0926 + 0.7783 0.5634 0.3639 + 0.7050 0.2093 0.0664 + 0.2874 0.9361 0.8809 + 0.5040 0.0051 0.3799 +
 
% Create electrode table region referencing electrodes 0, 1, and 2
shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
'table', types.untyped.ObjectView(electrodesDynamicTable), ...
'description', 'shank0', ...
'data', (0:2)');
 
% Define spike event series for unsorted spike times
spike_events = types.core.SpikeEventSeries( ...
'data', spike_snippets, ...
'timestamps', (0:19)', ... % Timestamps for each event
'description', 'events detected with 100uV threshold', ...
'electrodes', shank0_table_region ...
);
 
% Add spike event series to NWB file acquisition
nwb.acquisition.set('SpikeEvents_Shank0', spike_events);

Detected Events

If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in ElectricalSeries objects in the acquisition group, and use the EventDetection class to identify the spike events in your raw traces.
% Create the EventDetection object
event_detection = types.core.EventDetection( ...
'detection_method', 'thresholding, 1.5 * std', ...
'source_electricalseries', types.untyped.SoftLink(raw_electrical_series), ...
'source_idx', [1000; 2000; 3000], ...
'times', [.033, .066, .099] ...
);
 
% Add the EventDetection object to the ecephys module
ecephys_module.nwbdatainterface.set('ThresholdEvents', event_detection);

Storing Spike Features (e.g Principal Components)

NWB also provides a way to store features of spikes, such as principal components, using the FeatureExtraction class.
% Generate random feature data (time x channel x feature)
features = rand(3, 12, 4); % 3 time points, 12 channels, 4 features
features = permute(features, [3,2,1]); % reverse dimension order for matnwb
 
% Create the FeatureExtraction object
feature_extraction = types.core.FeatureExtraction( ...
'description', {'PC1', 'PC2', 'PC3', 'PC4'}, ... % Feature descriptions
'electrodes', electrode_table_region, ... % DynamicTableRegion referencing the electrodes table
'times', [.033; .066; .099], ... % Column vector for times
'features', features ...
);
 
% Add the FeatureExtraction object to the ecephys module (if required)
ecephys_module.nwbdatainterface.set('PCA_features', feature_extraction);

Choosing NWB-Types for Electrophysiology Data (A Summary)

As mentioned above, ElectricalSeries objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the ElectricalSeries class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the API documentation and the section on Extracellular Physiology in the "NWB Format Specification" for more details on using these objects.
For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with ElectricalSeries objects as acquisition data, and use the EventDetection class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use SpikeEventSeries objects.
The results of spike sorting (or clustering) should be stored in the top-level Units table. The Units table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns waveform_mean, waveform_sd, and waveforms in the Units table to store individual and mean waveform data.

Writing the NWB File

nwbExport(nwb, 'ecephys_tutorial.nwb')

Reading NWB Data

Data arrays are read passively from the file. Calling TimeSeries.data does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. load with no input arguments reads the entire dataset:
nwb2 = nwbRead('ecephys_tutorial.nwb', 'ignorecache');
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data.load;

Accessing Data Regions

If all you need is a data region, you can index a DataStub object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.
% read section of LFP
nwb2.processing.get('ecephys'). ...
nwbdatainterface.get('LFP'). ...
electricalseries.get('ElectricalSeries'). ...
data(1:5, 1:10)
ans = 5×10
0.9651 -0.8883 -0.0094 0.8173 -1.0690 0.1127 -1.4322 -1.0934 -1.5671 1.0822 + -0.6077 0.1766 0.1769 0.3961 -0.4618 -0.6057 0.4036 -0.8789 -1.2575 0.5712 + -0.1929 0.5733 -1.4192 -0.2441 1.7385 -0.8977 -0.4516 -0.9052 -0.1145 0.3583 + 0.2470 -0.4401 1.6814 -0.3924 -0.1789 0.1862 -0.4147 -0.1626 -0.4556 -0.1766 + 0.2099 -0.4356 -0.8001 -0.3095 -1.3299 0.1646 0.5666 1.0098 -0.4388 0.7080 +
 
% You can use the getRow method of the table to load spike times of a specific unit.
% To get the values, unpack from the returned table.
nwb.units.getRow(1).spike_times{1}
ans = 24×1
0.3390 + 0.8821 + 0.0155 + 0.9190 + 0.3082 + 0.1643 + 0.0970 + 0.8547 + 0.1262 + 0.3271

Learn more!

See the API documentation to learn what data types are available.

MATLAB tutorials

Python tutorials

See our tutorials for more details about your data type:
Check out other tutorials that teach advanced NWB topics: