Chapter 2. Using Klusters

Table of Contents

Starting a New Session
Views and Displays
Cluster View, Waveform View, Correlation View
The Cluster View
The Waveform View
The Correlation View
Trace View
Browsing Wide-Band Data
Browsing Unit Activity
Adjusting Channel Amplitudes
Labels
Setting the Start Time from a Cluster View
Discarding Noise and Artefacts
Grouping and Reshaping Clusters
Grouping Assistant
Automatic Reclustering
Cluster Information
Saving the Cluster File
Using Klusters over a Slow Network Connection
Printing and Exporting
Printing
Exporting

Starting a New Session

To start editing a file, select File->Open. In the Open dialog, you can select any type of data file (clu, fet, spk, or par: see File Formats).

A session can also be started from the command line, by typing:

% klusters filename

Once the data is loaded, the main window should look like this:


Overview.

The Cluster Panel (left) lists all the cluster IDs already defined in the cluster file. Clusters can be selected by clicking on the colored squares. Use Shift or CTRL for multiple selections (see the Edit menu for more choices).

The rest of the Main Window shows an Overview Display of the data. This is one of several kinds of display that Klusters uses to present the data. It actually combines three views: a Cluster View displaying spikes in the PCA feature space (left), a Waveform View (top right), and a Correlation View displaying auto- and cross-correlograms (bottom right).

Notice that each cluster has its own color: the same color is consistently used in the Cluster Panel, the Cluster View, the Waveform View, and the Correlation View. This makes it easy to quickly identify a given cluster across different views. If you wish to change the color of a cluster (e.g. when simultaneously displaying several clusters with similar colors), middle-click on the corresponding colored square in the Cluster Panel to open the Color Chooser. As soon as you validate your new choice, all views will be updated accordingly (but see Using Klusters over a Slow Network Connection).

Note

Keep in mind that working on large files requires a large amount of RAM and a powerful CPU.