28
Vb
Wbll
WblF1
Wb21
Wb2F2
WbCl
∖WbCFc /
or, more concisely,
(vb + Kb dbll <⅛12
Φbll ≠frll
Φbl2 Ψbl2
∖ ΦbCFc
dbCFc ∖
ΨbCFc /
( Vb >
Wbii
WfeiFi
W>b21
Wb2F2
WbCl
∖WbCFc∕

∂tzb Qb^,b T ub-
(2.32)
We discretize the neuron in space by dividing each branch into 7ь = ceil(f⅛∕∕ι) com-
partments, where h is some desired step size. The connectivity of the full morphology
is encapsulated in the Hines matrix H, which is the spatial discretization of each T>b
coupled with (2.19) to (2.21) and (2.24) (Hines, 1984). More detail about constructing
this matrix will be given in §A.
Using the Hines matrix imposes an outside-in ordering of branches and compart-
ments, which leads to minimal fill-in for Gaussian Elimination (Hines, 1984). If m
and n denote, respectively, the number of gating variables per compartment and the
total number of compartments, i.e.,
с в
m = and n = 1 + 7ь>
c=l b=l
More intriguing information
1. BUSINESS SUCCESS: WHAT FACTORS REALLY MATTER?2. The name is absent
3. The name is absent
4. Kharaj and land proprietary right in the sixteenth century: An example of law and economics
5. SOME ISSUES CONCERNING SPECIFICATION AND INTERPRETATION OF OUTDOOR RECREATION DEMAND MODELS
6. The Making of Cultural Policy: A European Perspective
7. An Efficient Circulant MIMO Equalizer for CDMA Downlink: Algorithm and VLSI Architecture
8. Trade Openness and Volatility
9. Optimal Taxation of Capital Income in Models with Endogenous Fertility
10. Integrating the Structural Auction Approach and Traditional Measures of Market Power