Electrical diversity of cortical interneurons is well known. Standardized classification called PING. They analyzed ~500 neuron's electrical responses and used statistical hierarchical classification to subdivide neurons into e-types. They use 38 features to describe the voltage responses, and use clustering methods to put the cells into groups.
Features were extracted from cell trace (e.g. AP amplitude, half-width, rate, adaptation, ISI), then each cell represented by a vector in m-dimensional space. 466 data-points from recordings of cells. PCA was used to determine the prominent components.
Each class is treated as a multidimensional Gaussian. Nested clustering analysis is performed by repeatedly applying k-means clustering. Here's the process:
PCA(Data) -> 10 Features -> 2 clusters of Data -> repeat for each cluster.
PCA classification pulls out many of the clusters created by subjective PING analysis. The data appears to be naturally high-dimensional as 10 PCs are needed to explain 80% of the variance. PCA shows some seperation of the subjective types, but not complete classification. This is just due to the biases in the data for different features. PCA doesn't pull out classification features necessarily.
Used linear disciminant analysis on data to find dimensions that best seperate the classes. LDA is supervised, thus requires the subjective classification to work.
Unsupervised nested clustering of the features lead to a hierarchical picture of neuronal e-type. The first split caused the majority of interneurons to break from the Pyramids. The next split seperated the FS cells from the adapting cells. Then the groups were further split and named as in Figure 8:
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