analysis:adrlab
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| * *.Ncs files: continuously sampled data, "local field potentials" | * *.Ncs files: continuously sampled data, "local field potentials" | ||
| - | * *.ntt files: raw tetrode files, you will generally not need to load these | + | * (*.ntt files: raw tetrode files, these are generally not included) |
| * *.Nev file: raw Events file | * *.Nev file: raw Events file | ||
| * *.Nvt file: video tracking file (usually zipped) | * *.Nvt file: video tracking file (usually zipped) | ||
| Line 12: | Line 12: | ||
| * *.t files: spike times for one putative neuron (output from MClust) | * *.t files: spike times for one putative neuron (output from MClust) | ||
| * *.clusters: cluster information (output from MClust, you will generally not need this) | * *.clusters: cluster information (output from MClust, you will generally not need this) | ||
| - | * *wv.mat and *ClusterQual.mat files: waveforms and cluster quality metrics | + | * *wv.mat and *%%ClusterQual%%.mat files: waveforms and cluster quality metrics |
| - | * *Metadata.mat file: structured task-related information such as locations of feeders, times of rewards, etc. | + | * *Metadata.mat file: structured task-related information such as locations of feeders, times of rewards, etc. (not included in all sessions) |
| - | * *ExpKeys.m file: basic information about this recording session, such as task, start time, end time, etc. (note this is a .m file, so needs to be run rather than loaded as you would a .mat file) | + | * *%%ExpKeys%%.m file: basic information about this recording session, such as task, start time, end time, etc. (note this is a .m file, so needs to be run rather than loaded as you would a .mat file) |
| + | * *vt.mat file: pre-processed video tracking data | ||
| Note the naming convention for the above files: all files are identified by the RYYYY-MM-DD string in the filename, and recording files are further named with the tetrode number. | Note the naming convention for the above files: all files are identified by the RYYYY-MM-DD string in the filename, and recording files are further named with the tetrode number. | ||
| ===== Basic data loading ===== | ===== Basic data loading ===== | ||
| + | |||
| + | The below will only work if you have grabbed the vandermeerlab code base and added it to your MATLAB path ([[computing:matlabsetup|instructions]]). If you are using a Mac, you will need to get the data loading files from Neuralynx and add them to the relevant folders (you'll know where when you get errors upon trying to load something). | ||
| + | |||
| + | First, change your current (working) folder to the recording session of interest. | ||
| + | |||
| + | Spike time series from a single cluster (putative neuron) are stored in a *.t file. These files are generated by MClust after the spike sorting process is complete. | ||
| + | |||
| + | To load all spike trains in a folder: | ||
| + | |||
| + | <code matlab> | ||
| + | >> S = LoadSpikes(FindFiles('*.t')); | ||
| + | </code> | ||
| + | |||
| + | this gives a cell array S with one cell for each spike train, stored in a ts object: | ||
| + | |||
| + | <code matlab> | ||
| + | >> plot(Data(S{1}),0,'.k'); % crude plot | ||
| + | </code> | ||
| + | |||
| + | This plots the spike times of the first cluster. A ts object is a custom (not built in into MATLAB) object with a number of methods (functions): the most useful are Data (get the spike times stored in it) and Restrict (restrict the spike times to specific times). | ||
| + | |||
| + | To load and plot a CSC file: | ||
| + | |||
| + | <code matlab> | ||
| + | >> csc = LoadCSC(FindFile('*CSC1a.Ncs'),'ConvertToS',1); | ||
| + | >> plot(Range(csc),Data(csc)); | ||
| + | </code> | ||
| + | |||
| + | Note that %%LoadCSC%% returns a tsd object ("timestamped data") which consists of matching arrays of times and data. As with ts objects, Range() returns the times, but now there is also Data() which returns the data. | ||
| + | |||
| + | Be aware that this time series can be restricted to the time the rat was actually freely moving and engaged in behavior; use the %%ExpKeys%% file and the Restrict function explained below to do this. | ||
| + | |||
| + | To access the position data, first load the *vt.mat file (which contains the x and y tsd objects) and then do: | ||
| + | |||
| + | <code matlab> | ||
| + | x_data = Data(Vt.x); | ||
| + | y_data = Data(Vt.y); | ||
| + | plot(x_data,y_data,'.'); % notice this has some extra tracking data outside of the track | ||
| + | </code> | ||
| + | |||
| + | We will now use the %%ExpKeys%% file and Restrict to only include data when the rat was actually on the track: | ||
| + | |||
| + | <code matlab> | ||
| + | run(FindFile('*keys.m')); % load experimental metadata; notice this is a script so we can run it | ||
| + | x = Restrict(Vt.x,ExpKeys.TimeOnTrack,ExpKeys.TimeOffTrack); % only include data from when rat was on track | ||
| + | y = Restrict(Vt.y,ExpKeys.TimeOnTrack,ExpKeys.TimeOffTrack); | ||
| + | plot(Data(x),Data(y),'.'); % looks better now | ||
| + | </code> | ||
| + | |||
| ====== Description of the Multiple-T task ====== | ====== Description of the Multiple-T task ====== | ||
| Line 44: | Line 93: | ||
| Linpath: [1x1 struct] | Linpath: [1x1 struct] | ||
| </code> | </code> | ||
| - | |||
analysis/adrlab.1372863200.txt.gz · Last modified: 2018/07/07 10:19 (external edit)
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