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Apply the recursive recognition process including the two measurement detection and updating in parallel at each PE.
The updating of the neuronal states can be operated: - either sequentially, after having chosen a certain order for the neurones, - or block-sequentially, by operating the updating in parallel in each sub-network of a partition of R and by afterwards activating these sub-networks sequentially, - or in a massively parallel fashion if only one sub-network exists.
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Thus node activations are effectively updated in parallel.
Next, lateral avalanching (in the y-direction) was determined for each cell and updated in parallel.
All top nodes in the layer can be updated in parallel, since none of the computed values affects the values of other nodes being updated.
In two spatial dimensions, lines of nodes can be updated simultaneously, and in three dimensions an entire layer of nodes can be updated in parallel [30, 31].
To avoid any memory interference, the nodal values of neighbouring subdomains are updated in parallel in one direction at a time.
The key strength of the application specific dynamical systems (ASDS) approach lies in the fact that all variables are updated in parallel.
On each multigrid level, we employ a block-Jacobi smoother, which maintains algorithmic scalability as all elements can be updated in parallel.
First, the ghost nodes belonging to subdomains with larger i values are updated in parallel, and thereafter ghost nodes in subdomains with larger j, k, and smaller i, j, k indices are updated.
After that, visible units are updated in parallel to get a "reconstruction" by Eq. (7), as illustrated in Figure 2(b), and the hidden units are updated again [44].
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