Table 2.

List of variables and mathematical notation used in this article

Xj	Discrete random variable representing primary input j(j = 1,2,3)
Xj	Specific activity level of primary input
nx	Number of primary inputs (= 3)
X	Discrete random vector representing primary input [X1X2X3]
Yk	Discrete random variable representing modulatory input k(k = 1,2,3)
yk	Specific activity level of modulatory input
ny	Number of modulatory inputs (= 3)
Y	Discrete random vector representing modulatory input [Y1Y2Y3]
Zi	Random variable specifying activity of DSC unit i (i = 1,2,..., 100)
zi	Specific activity level of DSC unit i
nz	Number of DSC units (= 100)
T	Random variable representing the target
t	Target state (t = 0,1,...., 7)
ps	Modality-specific target probability
pc	Cross-modal target probability (pc = ½ − ps)
px0, px1	Spontaneous and driven activation probabilities for primary input Xj
py0, py1	Spontaneous and driven activation probabilities for modulatory input Yk
p	Probability that a binary random variable takes value 1
n	Total number of binary random variables for binomial process
b(n, p)	Binomial distribution with parameters n and p
r	Number of binary variables taking state 1 for binomial process
uij	Unmodulated weight of primary input j onto DSC unit i
Wij	Modulated weight of primary input j onto DSC unit i
Vijk	Weight of the modulatory input k onto the primary input connection uij
dijk	Dummy variable for accumulating Vijk
α	Learning rate for stage-one training
β	Learning rate for stage-two training
θu	Primary weight threshold for stage-one training
θx	Primary input threshold for stage-two training
θy	Modulatory input threshold for stage-two training
θz	DSC unit threshold for stage-two training
ψ	Sum of active DSC units with activity zi exceeding threshold θz
θl	DSC unit threshold for information gain computation
h	Index for DSC units in a neighborhood
P	Probability
Dx	Kullback-Leibler divergence measure for primary input
Dy	Kullback-Leibler divergence measure for modulatory input
I	Information in bits
H	Entropy in bits
ϕ	Tonic, bias input (= 10)
γ	Squashing function sensitivity (= ⅕)
←	Update symbol
exp	Exponential
Σ	Summation
!	Factorial