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The learning rate of networks is significantly affected by the data presented to artificial neural networks.
In such a case, Eq. (4) would provide a lower bound for the mutual information rate of networks with nodes that have non equal dynamics.
This observation is unlikely to explain our results as the mean firing rate of E cells in networks that generated grid firing fields (grid score >0.5, networks with gE or gI set to 0 excluded) was in fact lower than the firing rate of networks without grid fields (1.2; 1.0; 1.0 Hz grid fields vs 3.0; 2.7; 1.2 Hz no grid fields, in networks with σ = 0; 150; 300 pA respectively).
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Therefore, the decrement rate of network coverage can be controlled and considerably reduced.
The rate of network infection is reduced in both cases showing that both scores provide useful targets for limiting spread.
At high irradiation intensity (Io = 20 mW/cm2), the rate of network formation prohibited significant phase separation, even when thermodynamically preferred.
With the increase of network scale, beacon cluster routing algorithm shows lower growth rate of network delay while the flooding obviously deteriorates network transmission efficiency.
n = 0.5 indicates case I, which is a perfect Fickian process, whereby the rate of network relaxation is faster than the rate of diffusion.
Cmin(G) is the minimum cut of G, and the capacity Cmin(G) is the maximal possible information rate of network G.
The rate of network infection is reduced in both cases showing that both scores provide useful targets for limiting the spread.
There were significant differences in the rate of network formation in the two neat systems, as the rate of anhydride curing of the epoxy was extremely slow at the temperatures required for reasonably measurable dicyclopentadiene (DCPD) ring-opening metathesis polymerization.
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