# Table 1

The nine prototypical ILD functions
Seven data normalization methods The nine prototypical ILD functions (min: 0.0 and max: 100, with ±6 %)
(A) (B) (C) (D) (E) (F) (G) (H) (I)
(1) Vn (i, j) = Xn(i, j) − μn -46/49 -67/68 -69/48 -37/59 -23/73 -43/71 -64/60 -42/46 -5.7/8
(2) Vn (i, j) = Xn(i, j)/max{max{Xn(i, j)}} 0.01/1 0.01/1 0.01/1 0.01/1 0.01/1 0.01/1 0.01/1 0.01/1 0.01/1
(3) Vn (i, j) = Xn(i, j)/max{Xn(i, j)} 0.01/1 0.01/1 0.01/1 0.01/1 0.01/1 0.01/1 0.01/1 0.02/1 0.24/1
(4) Vn (i, j) = Xn(i, j)/σn 0.04/2 0.02/2 0.04/2 0.06/2 0.04/3 0.03/3 0.02/2 0.1/23 0.6/51
(5) Vn (i, j) = log2(Xn(i, j)) − log2n) -4.6/1 -6/1.7 -5.3/1 -4/1.3 -4.3/2 -4.6/2 -4.8/1 -4.4/1 -1/0.7
(6) Vn (i, j) = 0.1/7 0.1/7 0.08/7 0.09/7 0.08/7 0.09/7 0.09/7 0.23/7 0.1/7.
(7) Vn (i, j) = -1.1/1 -1.1/1 -1.8/1 -1/1.7 -0.7/2 -1.3/2 -1.5/1 -1.8/1 -1.4/1

Where, the number of raw and number of columns for the matrix form of normalized “Vn” and raw data “Xn” are both comprise same “i” number of raw, “j” number of columns and “n” number of ILD patterns. These nine prototypical ILD functions are: (A) Sigmoidals w/varying # of spike counts, (B) Sigmoidals w/varying position of cut-off, (C) Sigmoidals w/varying steepness of the slope, (D) Peaked w/ varying # of spike counts, (E) Peaked w/ varying position of cut-off, (F) Peaked w/ varying steepness of the slope and position of cut off, (G) Peaked w/ unilateral transition to Sigmoidal, (H) Peaked w/ bilateral transition to Intensive, and (I) Intensive w/ varying number of spike counts. The seven data normalization methods are: (1) Mean correction, (2) Overall maximum value, (3) Each vector maximum value, (4) Standard deviation, (5) Logarithmic, (6) Unit total probability mass, and (7) Data standardization. In addition, seven data normalization methods (with the equations) were applied to nine (from “A” to “I”) prototypical ILD functions. The result was presented in this table with the minimum of minima (four vectors)/ maximum of maxima (four vectors) values were all shown in spike counts.

Uragun and Rajan

Uragun and Rajan BMC Neuroscience 2013 14:114   doi:10.1186/1471-2202-14-114