The connection length between two connected neurons was the Euclidean distance between the centers of neurons assuming that the distance between the growth cone and the target neuron was negligible.
We also modeled motion parameters in the first level, corrected for motion parameters when extracting the regional time series, and found that connectivity differences were not restricted by connection length (a feature of motion-induced artifactual connectivity changes).
A major finding of this study is the identification of two network architectures, with either a gradient of local connectivity or a gradient of long-distance connection length, that give rise to activity patterns with localized timescales.
Each sphere is connected to its adjacent neighbors, so the total connection length is zero.
In this paper, we show that associative memory networks with small-world architectures can provide the same retrieval performance as randomly connected networks while using a fraction of the total connection length.
For each subject, the cortico-cortical connectivity was evaluated for these data using the same grids and minimum connection length as for the real data.
The main test parameters included steel grade, connection length and out-of-plane eccentricity.
The connection length from the edge to the target plate is in the range of 50 400 m.
Fig. 5 Relative strengths (alpha_{rc}/alpha_{r}) of different connection types as a function of the connection length r.
The approach is based on controlling the maximum connection length between processing nodes in order to make fully parallel implementation feasible.
The output results are efficient connection length of left side and right side, top width (WT) and bottom width (WB) of the weld bead.
