Categorical encoding of decision variables in orbitofrontal cortex
Fig 2
Illustration of the procedure for category discovery.
For the original rates (left), each axis of the space denotes the firing rate of the cells in the various trial types. Each data point in this space represents a cell. For illustration, we present only 3 of the 9 dimensions. The original rates are then centered and normalized to unit length. The normalization effectively moves the points to the surface of a hyper-sphere. The points are then clustered using spherical k-means for a given number of clusters and centroid clustering for given variables. In the illustration, variables are represented as larger points. The resulting partitions are compared using the adjusted mutual information measure as a function of the number of clusters and number of variables (right).