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The mapping is controlled by the selection network.
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The core mechanism of this mapping is a gating mechanism implemented with sigma-pi units [ 26] which are controlled by layers in the selection network (see Fig. 3 for an illustration and " Appendix" for details on the operations of the contents network).
The selection network aims to select a stimulus in the input image by producing an appropriate activation pattern.
A good way of conceptualizing the reason for this behaviour is that the speed of convergence of the competitive process in the selection network by and large determines the VS-SAIM's reaction times. 2 Moreover, the speed of convergence is proportional to the contrast between activations in the matching surface.
This input results from terms in the energy function which ensure that VS-SAIM's behaviour including the behaviour of the selection network is constraint by the input image and the template-knowledge implemented in the knowledge network.
The way the matching network projects into the selection network following Eq. 37.
Therefore, the sigmoid function was used in the selection network and the knowledge network.
The function of the matching network is to mediate the feedback from the knowledge network to the selection network.
The selection of the output elements that comprise the next output vector by the element selection network is indicated by underlining of the selected elements.
Actually, quantification of the brain network was directly affected by parcellation strategies, such as connection weights, which limit the selection of network models and parameters.
We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com