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Data for a scaling parameter <i>α</i> of the recurrent time of power law with...
Scaling parameter α and Akaike information criterion (AIC) weights of power law for t−α. t means the recurrence time for the mean degree of an EL net- work. Data for a scaling... -
r37980778c78--9a5e95a136464d5332d3adb856e295cc
In real networks, the resources that make up the nodes and edges are finite. This constraint poses a serious problem for network modeling, namely, the compatibility between... -
Parametric values used for the Eqs (5,8 and 9), with <i>η</i><sub>D2</sub> = ...
Parametric values used for the Eqs (5,8 and 9), with ηD2 = .1, ηD1D2 = 0.1, and ηD1 = .01. -
r37980778c78--eff3ce2c0b8d59ad35962fe4c86b46a5
This supporting document contains the following components of our analysis. (i) Formal proofs of the MGEO-P properties. (ii) Full statistical information about each of the... -
Striatal MSNs and different sensitivities of decision making.
Striatal MSNs and different sensitivities of decision making. -
Statistical data for an in-degree and an out-degree distribution.
The above table means t-test for relation between an in-degree distribution e − λ in x and an out- degree distribution e−λoutx. The bellow one means Mann-Whitney U... -
r37980778c78--a67d0d935ff03a1a5b9ea1aa312583cc
Scaling parameter α and Akaike information criterion (AIC) weights of power law for w−α. w mean an edge’s weight. Data for a scaling parameter α of weight of power law with AIC... -
Dimension scaling for Facebook and LinkedIn.
The specific dimensional scaling lines fit to the data in Figures 4 and 5 illustrate the growth of the network is logarithmic in the number of nodes. Dimension scaling for... -
Network dimensions.
Table shows number of neurons per layer, number of synapses to preceding layer and size of receptive field from which the connections are received.
