Sentence examples for in which connectivity from inspiring English sources

Space networks, in which connectivity is deterministic and intermittent, can be modeled by delay/disruption tolerant networks.

A graph theoretical network analysis was carried out to evaluate small world networks, in which connectivity was determined by all pair-wise combinations of channels resulting in 14 × 14 connectivity matrices of phase synchronisation.

This configuration mimics a well-mixed population in which connectivity is limited.

We obtain similar results in implementations of E-I models in which connectivity is probabilistic and in models extended to include additional I to I and E to E connections.

Whereas SBCA and partial correlation analyses focused on nodes of the fronto-parietal grasping-network, this approach has the potential to identify any networks (defined as areas sharing BOLD signal temporal correlations) in which connectivity is changing as a result of the TMS intervention.

This hypothesis requires models of effective connectivity, in which connection strengths vary as a function of the associative strength predicted by the learning model.

We find that the rate at which connectivity degrades in such circuits is nearly constant up to a missing-crossbar fraction f of about 50%, for both site and bond percolation.

Different from traditional fully connected recurrent neural networks, ESN adopts the sparsely connected hidden layer in which the connectivity and weights of hidden neuron nodes are randomly assigned and unchanged during training.

Instead, marked effects in the posterior cingulate cortex were evident in response to a task designed to engage the negative attentional bias in MDD [ 24], and there were time-dependent changes in the DMN in MDD patients in which increased connectivity towards limbic regions but decreased connectivity with lateral cortical regions emerged as treatment progressed.

Different from the traditional fully connected recurrent neural networks, an echo state network (ESN) adopts the sparsely connected hidden layer in which the connectivity and weights of hidden neuron nodes are fixed and randomly assigned.

Each of these models contains all the currently known connectivities, shown in Figure 1, but differs in which unknown connectivity is considered.