By James A. Freeman
Freeman and Skapura offer a pragmatic creation to synthetic neural platforms (ANS). The authors survey the commonest neural-network architectures and express how neural networks can be utilized to unravel real medical and engineering difficulties and describe methodologies for simulating neural-network architectures on conventional electronic computing platforms.
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Additional info for Neural Networks: Algorithms, Applications, and Programming Techniques (Computation and Neural Systems Series)
The input connections are modeled as arrows from other processing elements. tj, called a weight. There is a single output value, which can fan out to other units. analogies, we shall now ignore our own advice and make a few ourselves. For example, like a real neuron, the PE has many inputs, but has only a single output, which can fan out to many other PEs in the network. The input the zth receives from the jth PE is indicated as Xj (note that this value is also the output of the jth node, just as the output generated by the ith node is labeled x^).
Notice the index we now associate with the weights array. This index indicates that there will be many such arrays in the network, each containing a set of connection weights. The index here indicates that this array is one of these connection arrays—specifically, the one associated with the inputs to the ith network unit. We will expand on this notion later, as we extend the data structures to model a complete network. The process needed to compute the aggregate input at the zth unit in the upper layer, net;, is as follows.
Layer to the association layer, and of inhibitory connections between R units. 13 for a simple perceptron with two R units. This drawing shows that each R unit inhibits the A units in the complement to its own source set. Furthermore, each R unit inhibits the other. These factors aid in the establishment of a single, winning R unit for each stimulus pattern appearing on the retina. The R units respond in much the same way as do the A units. If the sum of their inputs exceeds a threshold, they give an output value of +1; otherwise, the output is — 1 .