Table 1.

A table listing the classification decision functions for the four different classifiers used in this paper

A. Poisson Naive Bayes (PNB) Embedded Image
B. Total Activity (TAct) Embedded Image
C. Maximum Correlation Coefficient (MCC) Embedded Image
D. Minimum Angle (Min Ang) Embedded Image

  • wc is a vector that is the mean of the training data from class c, w̄c is a scalar that is the mean of wc, x is the test vector to be classified, x̄ is a scalar that is the mean of x, and n is the number of neurons; thus training the classifier consists of learning wc and w̄c and testing the classifier consists of determining which class x belongs to. As can be seen, all these classifiers are rather similar and mainly differ in how they normalize the data and, consequently, whether they take the overall level of population activity into account (A and B) or whether they only examine relative differences in the firing rate activity between neurons (C and D). TAct, Total Activity; MCC, Maximum Correlation Coefficient; Min Ang, Minimum Angle.