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--- analysis:nsb2014:week5 [2014/07/20 18:18]
mvdm [Loading some data]
+++ analysis:nsb2014:week5 [2018/07/07 10:19] (current)
@@ Line 33: / Line 33: @@
 Even if you do not go as far as reading it, do at least make your figure legends big enough to be readable! :-)
-Step-by-step:
 ==== Loading some data ====
@@ Line 57: / Line 55: @@
 S = LoadSpikes(cfg)
 </code>
+If you are using your own data that has a missing or bad .clusters file, do this:
+<code matlab>
+cfg = [];
+cfg.useClustersFile = 0;
+S = LoadSpikes(cfg)
+</code>
 This should give a data structure containing spike train data:
@@ Line 98: / Line 105: @@
 ''csc'' is the common Neuralynx designation for "continuously sampled channel" and typically is an EEG or LFP type signal sampled and filtered so that high-frequency components such as spikes are not accessible. It is possible to have wide-band, 32kHz CSCs suitable for spike extraction, but these are not included in the current dataset. As discussed in [[analysis:nsb2014:week2|Module 3]] a LFP is defined by matching sets of sample timestamps (''csc.tvec'') and sampled data (''csc.data'').
-==== ts and tsd objects: basic methods ====
+==== ts objects: basic methods ====
-The ''S'' variable that contains our spike times is a MATLAB struct that conforms to the ''ts'' ("timestamp") format introduced in [[analysis:nsb2014:week2|Module 2: Introduction to Neuralynx data formats and preprocessing]]. This variable stores spike trains as lists of numbers, corresponding to the timestamps of the spikes. So, to plot a raster with the spikes of the first cell in our data set:
+The ''S'' variable that contains our spike times is a MATLAB struct that conforms to the ''ts'' ("timestamp") format introduced in [[analysis:nsb2014:week2|Module 2]]. Its ''.t'' field contains spike trains as lists of numbers, corresponding to the timestamps of the spikes. So, to plot a raster with the spikes of the first cell in our data set:
 <code matlab>
@@ Line 109: / Line 116: @@
 </code>
-Notice that the ''S'' variable also contains labels for each cell. We can use this to add a title to the rasterplot:
+☛ Extend the example code above to make a rasterplot containing the spikes for all cells in ''S''. To facilitate re-use later, wrap your code in a function, %%PlotSpikeRaster%%, that plots the raster for any ''ts'' input.
-<code matlab>
+You should find that it takes a significant amount of time to plot that many spikes. In practice we often want to plot spikes for specific time intervals.
->> h = title(S.label{nCell}); set(h,'Interpreter','none');
-</code>
+A useful function that works on ''ts'', ''tsd'' and ''iv'' objects is ''restrict()''.
+☛ Use MATLAB's ''help'' function to look up the usage information for ''restrict()''. In a new editor cell, use this function to create a ''S_r'' variable with data restricted to between 5950 and 6050 s.
-☛ What happens if you don't set the interpreter to none?
+Calling your %%PlotSpikeRaster%% function on this restricted data should be much faster. Now that we are a bit more zoomed in, some interesting features of the rasterplot become visible. For instance, at time 5967, many cells are synchronously active. Could this be a sharp wave-ripple event (SWR)?
-A useful method that works both on ''ts'' and ''tsd'' objects is ''Restrict()''.
+☛ Restrict the csc to the same time (5950 to 6050) and plot it in the same figure. You can use the ''rescale()'' function to fit the csc into a convienient data range (e.g. ''rescale(csc.data,-5,0)'').
-☛ Use MATLAB's help function to look up the usage information for ''Restrict''. In a new cell, use it to create a ''csc_short'' variable with data restricted to between 5950 and 6050 s.
+You should see that the synchronous activation around time 5967 is indeed associated with a striking oscillation in the LFP:
+{{ :analysis:nsb2014:m5_swr_example.png?600 |}}
-==== Plotting with handles ====
+This high-frequency osillation (the "ripple") superimposed on a slower oscillation (the "sharp wave") is the signature associated with "replay" events in the hippocampus. We will explore this phenomenon in detail in later modules.
+==== Figure properties, handles, and exporting ====
-☛ Plot the LFP (time against voltage) for the segment between 5950 and 6050 s. Remember to do this using a cell in your ''sandbox.m'' file so that it's easy to make changes later and you keep a record of what you did.
+(Remember to execute the commands below from your ''sandbox.m'' file, not from the command line! That way you can more easily retrieve what you did later.)
-Any MATLAB figure is actually a collection of objects, all with their own properties which you can access and edit. For instance, to change the figure background to black, do
+A MATLAB figure is actually a collection of objects, all with their own properties which you can access and edit. For instance, to change the figure background to black, do
 <code matlab>
@@ Line 134: / Line 145: @@
 ''gcf'' is MATLAB's name for "get current figure". The above command set its '''Color''' property to ''[0 0 0]'' which is black (zero red, zero green, zero blue in the RGB world).
-☛ Set the background of the current /axes/ to black (''gca'' is MATLAB's name for the current axes object; notice that the axes background is currently white, which looks ugly).
+☛ Set the background of the current //axes// to black (''gca'' is MATLAB's name for the current axes object; notice that the axes background is currently white, which looks ugly).
 Notice that now you can't see the axis lines and labels anymore, because they too became black when you changed the color of the axes. You want to set their color to be white, but you might not know the name of the relevant property.
@@ Line 150: / Line 161: @@
 ☛ Look at the image file. The colors are all wrong! Solve this problem by turning off the ''InvertHardCopy'' property of the figure, and save again. (MATLAB does this by default to facilitate printing images on white paper.)
-Let's add some more features to our plot.
+Let's add some moderately fancy features to our plot. Starting from a currently selected rasterplot, do the following:
 <code matlab>
-hold on; box off;
+%% add multi-unit activity to rasterplot
+ax1 = gca; ax1_pos = get(ax1,'Position');
+ax2 = axes('Position',ax1_pos); % new axes with same position as first
-csc_mean = nanmean(Data(csc));
+cfg = []; cfg.tvec = csc.tvec; cfg.sigma = 0.1;
-xr = get(gca,'XLim');
+mua = getMUA(cfg,S); % obtain multi-unit activity
-mean_hdl = plot(xr,[csc_mean csc_mean]);
+xr = get(ax1,'XLim');
+mua_r = restrict(mua,xr(1),xr(2)); % only keep the data we need
+axes(ax2);
+mua_hdl = plot(mua_r.tvec,mua_r.data,'Color',[0.7 0.7 0.7]);
+set(gca,'YAxisLocation','right','Box','off','XTick',[],'Color','none')
+linkaxes([ax1 ax2],'x');
 </code>
 A few things to note here:
-  * ''hold on'' prevents our newly plotted object from erasing whatever was there before.
+  * Using the ''Position'' property of the current axes (ax1, containing the rasterplot) we created a new set of axes (ax2).
-  * ''box off'' speaks for itself.
+  * In these new axes, we plotted the multi-unit activity, obtaining an output argument. Like ''gca'' and ''gcf'' this is a **handle** to graphics object; in this case, a handle to the multi-unit activity signal we just drew.
-  * We used the ''XRange'' property of the current axes to get the smallest and largest x values in the plot.
+  * We set the properties of the new multi-unit axes to have the y-axis on the right (plus a few other properties).
-  * ''plot()'' was called with an output argument. Like ''gca'' and ''gcf'' this is a **handle** to graphics object; in this case, a handle to the line we just drew.
+  * ''linkaxes()'' was used to link the x-axis of both axes so that both update when zooming in. Try it!
-☛ Change the properties of ''mean_hdl'' so that the line is red, dashed, and of width 2.
+☛ Change the properties of ''mua_hdl'' so that the line is red, dashed, and of width 2.
 seconds of LFP is a lot to display.
@@ Line 173: / Line 193: @@
 ☛ Use the 'XLim' axes property to zoom in to 5989 to 5990 s; while you are at it, increase the font size to 24. (You can change multiple properties with the same command, just keep adding property-value pairs.)
-It would be nice to be able to update the figure without having to type these ''XLim'' commands. To do this we need to use a special Figure property, introduced in the next section.
+It would be nice to be able to update what time window of the data we are looking at, without having to type these ''XLim'' commands. To do this we need to use a special Figure property, introduced in the next section.
 One final point of interest: different graphics objects are often (hierarchically) related. For instance, our figure currently contains one set of axes. We can obtain the axes handle directly, ''axes_handle = gca;'', but also indirectly, ''axes_handle = get(gcf,'Children');''. This works because currently, the axes are the only child of the figure.
@@ Line 229: / Line 249: @@
 ☛ Verify your understanding by calling ''test_fun'' such that ''b'' and ''c'' are both set to ''0''. Also note what happens when you do ''test_fun(1,'B',1)''!
-==== Assignment ====
+A different way of flexibly specifying function inputs is through the use of a ''cfg'' (configuration) input. An example of how this works can be seen in the ''getMUA()'' function used above.
-☛ Implement a function ''neuroplot()'' as follows:
+==== Exercise ====
+☛ Extend your %%PlotSpikeRaster%% function as follows, using either the varargin or cfg strategy, and your knowledge of object handles.
 <code matlab>
-function neuroplot(spikes,csc,varargin)
+% required inputs:
-% function neuroplot(spikes,csc,varargin)
 %
-% inputs:
+% spikes: ts object with spike train
 %
-% spikes: {nCells x 1} cell array of ts objects (spike train)
+% optional inputs:
-% csc: {nCSCs x 1} cell array of tsd objects (LFPs)
 %
-% varargins:
+% csc: tsd object with LFP data (bonus points for accepting more than one gracefully)
 %
-% cscColor: [nCSC x 3] RGB values to plot CSCs, default []
+% cscColor: [nCSC x 3] RGB values to plot CSCs, defaults to red
-% spikeColor: [nCells x 3] RGB values to plot spikes, default []
+% spikeColor: [nCells x 3] RGB values to plot spikes, defaults to black
 %
-% evt: [nEvents x 1] event times to plot, default []
+% evt: ts object with event times to plot as vertical lines, defaults to none
-% evtColor: [nEvents x 3] RGB values to plot events, default []
 %
-% interactiveMode: boolean to enable/disable arrow key navigation
+% interactiveMode: boolean to enable/disable arrow key navigation of time axis
 </code>
-This function should produce a rasterplot of spikes -- one neuron per row -- with one or multiple LFPs plotted above it, similar to panel Aa of the Dragoi and Tonegawa figure above. The values on the y-axis are not important for now, so you can feel free to hide this axis and rescale everything to look good. (However, if you want to include a scalebar for the LFPs, go ahead!) Set the default x range to 2 seconds.
+This function should produce a rasterplot of spikes -- one neuron per row -- with one or multiple LFPs plotted above it, similar to panel Aa of the Dragoi and Tonegawa figure above. The values on the y-axis are not important for now, so you can feel free to hide this axis and rescale everything to look good. Set the default x range to 2 seconds.
-You can pick a default color to plot everything, but there should be optional arguments to have the user specify a color for each neuron and LFP.
+Also, we want to be able to overlay events of interest on this plot (such as times when the animal received reward, or brain stimulation was triggered); these can be simple vertical lines that extend all the way through the plot. You can make up a few event times yourself for testing.
-Also, we want to be able to overlay events of interest on this plot; these can be simple vertical lines that extend all the way through the plot. You can make up a few event times yourself for testing.
 Finally, for data exploration it is helpful to be able to skip through the data with the arrow keys; set it up so that the left and right arrows can be used to scroll left and right. For extra awesomeness, add zoom functionality that scales the LFPs when pressing the up and down arrows.
-☛ When you are satisfied with your work, save a copy in your ''shared'' folder and push it to GitHub. Verify that your version is visible from the web.
+☛ When you are satisfied with your work, save a copy in your project folder and push it to GitHub. Verify that your version is visible from the web.
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