PMC:7782580 / 53554-55434 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7782580","sourcedb":"PMC","sourceid":"7782580","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7782580","text":"Control Gate Block: As shown in Fig. 6c, the Control Gate Block was a multiple-input single-output block consisting of a predictor module, a counter module, and a synapses module to control the optimization direction while controlling the information flow in the framework. The pipeline of the predictor module is shown in Supplementary Fig. 19a, where the Input S1 is the output of the ResBlock-B. The Input S1 was then flattened to a one-dimensional feature vector as the input of the linear layer. The output of the linear layer was converted to a probability of each category using the softmax function. A sensitivity calculator used the Vpred and Vtrue as inputs to calculate the TP, TN, FP, and false-negative (FN) rates to calculate the sensitivity. The sensitivity calculation was followed by a step function to control the output of the predictor. The ths was a threshold value; if the calculated sensitivity was greater or equal to ths, the step function output 1; otherwise, the output was 0. The counter module was a conditional counter, as shown in Supplementary Fig. 19b. If the input n was zero, the counter was cleared and set to zero. Otherwise, the counter increased by 1. The output of the counter was num. The synapses block mimicked the synaptic structure, and the input variable num was similar to a neurotransmitter, as shown in Supplementary Fig. 19c. The input num was the input parameter of the step function. The ths was a threshold value; if the input num was greater or equal to ths, the step function output 1; otherwise, it output 0. An element-wise multiplication was performed between the input S1 and the output of the synapses block. The multiplied result was passed on to a discriminator. If the sum of each element in the result was not zero, the Input S1 was passed on to the next layer. 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