![]() Pour vous HYF Litchi Texture PU Sac à bandoulière en cuir Casual Messenger Bag Ladies sac à main (Noir) (Couleur : Black) SWR = IntersectIV ( ,SWR2,SWR1 ) % obtain intersection between intervals (both LFP power and MUA) MUA = getMUA (cfg,S ) % get multi-unit activity (need to load S with LoadSpikes())Ĭfg. SWR1 = TSDtoIV (cfg,cscF ) % detect intervals where z-scored power is > 3 SD above meanĬfg = cfg. merge_thr = 0.05 % merge events closer than thisĬfg. dcn = '>' % return intervals where threshold is exceededĬfg. decimateFactor = 4 % reduce sampling frequency to speed things up csc = decimate_tsd (cfg, csc ) ĬscF = LFPpower ( ,cscF ) % obtain instantaneous signal power (Hilbert transform)Ĭfg. t % cell array with filenames to load csc = LoadCSC (cfg ) Ĭfg = cfg. Next, let's get a better view of what the rat was actually doing by plotting the position data, with the spikes from an example neuron on top: There are some obvious differences with the previous plot, including the presence of a “theta” (~8Hz) oscillation in the LFP, and the rhythmic firing patterns of some neurons (likely “place cells”). T1 = run_start (iRun ) t2 = run_end (iRun ) Now let's look at a raster from when the rat was running on the track: If you zoom in on this, you will see a fast oscillation in the LFP (“ripple”) occurring at the time of this potential replay event. You can see some synchronous activation of a large number of cells showing up as a vertical stripe, for instance around time 5045.4. This particular segment is taken from a time when the rat was resting on a pedestal. The local field potential from one of our electrodes is shown underneath. The x-axis shows time in seconds, and the y-axis contains the spikes from our neurons, one per row. PlotSpikeRaster (cfg,restrict (S,t1,t2 ) ) If the above executes, you can now plot a raster of the spikes, along with the LFP: This excludes the baseline recording blocks taken at the beginning and end of each recording session. In this case, we are restricting the data to the times the rat was actually performing the task, specified in the ExpKeys. ![]() The restrict() function restricts the data to the given start and end times. ![]() %% load the data clear all pack cd ( 'C:\data\R042\R042-' ) % replace with your data location load (FindFile ( '*vt.mat' ) ) % position data (previously extracted and filtered from raw data) load (FindFile ( '*times.mat' ) ) % trial start and end data (previously generated from raw data) load (FindFile ( '*CoordL.mat' ) ) % used for linearizing position data The best way to do this is to create a sandbox.m file, create a new Cell for each block, paste the code into it, and hit Ctrl Enter. ☛ From now on, you should execute the code blocks provided. Let's combine the loaders above with a simple visualization. So, if you wanted to plot x against y, you could do plot(posdata.data(1,:),posdata.data(2,:),'.'), but a more general approach that doesn't require knowing which variable is which dimension is plot(getd(posdata,'x'),getd(posdata,'y')). Note that the data field now has dimensionality this is because there is both x and y data as indicated by the label field. This should be named Rxxx-yyyy-mm-dd-vt.mat from now on in this tutorial, you can simply use the already provided, previously saved file. Nvt files are large, it is often convenient to save this posdata variable as a. Nvt file is found in the current directory and loads that one:īecause the. zip archive by default.) If no filename is specified in the input cfg, LoadPos() checks if a single. (In your data folder, this raw file is in a. This loads raw Neuralynx position data (*.nvt). The TSD data type has the following fields: This is exactly what the timestamped data (TSD) data type is, as illustrated by the LoadCSC() function: Thus, what we need to fully describe such a signal is two arrays of the same length: one with the timestamps and the other with the values. The result of this is that instead of a truly continuous signal, we have instead a set of points, each with a timestamp and a value: ![]() Such signals are acquired through sampling, that is, a data point is acquired at some specific time, and then some time later, another point, and so on (the idea of sampling and some consequences are explored in detail in Module 3). Time-varying signals, such as extracellular potentials recorded by an intracranial electrode, or position data recorded by an overhead camera, are very common in (neuro)science.
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