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The individual functional connectivity graph was stored as a weighted connection matrix before clustering.
RBM is a bipartite graph shown in Fig. 2. It has a visible and hidden layer in which visible units that represent observations are connected to hidden units that learn to represent features using weighted connection.
The SNN model used in this work is the feed-forward network, each neuron is connected to all the neurons in the next layer by a weighted connection, which means that the output signal of a neuron has a different weighted potential contribution [22].
One node can bear a weighted connection to another node, also given by a real number.
For DEI, we also choose 11 thresholds for each predicted weighted connection matrix to get networks on various sparseness levels.
An SOM consists of a rectangular grid of clusters, each of which has a weighted connection to every input node (Fig. 1).
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In addition, there is also a bias with modifiable weighted connections, which is only connected to neurons in the hidden and output layers.
It only requires a list of weighted connections where the weight determines the importance of the connection.
Artificial Neural Networks, in particular Multilayer Perceptrons (here called simply Neural Networks, or NNs) are built up of three or more layers of nodes and weighted connections between them; weights are set maximizing the rate at which the output layer correctly identifies the true class of given examples.
Graph clustering detects clusters in networks by finding densely connected sets of nodes where weighted connections of nodes within the sets are stronger/denser than weighted connections between nodes inside and outside of the set.
In an ANN, information is trained and distributed into a computer network with an input layer, hidden layers and an output layer all connected in a given structure through weighted connections [23].
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