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Conceptually, under active inference, predictions about proprioceptive input are passed down the hierarchy; not motor commands.
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First, the effect of the input is passed forward through the network.
The analog video input is passed to an analog-to-digital converter, which encodes each pixel in YUV format.
If no feedback signal is provided the driving input is passed forward unchanged.
The input is passed to area V2 where long-range filters perform a grouping of elongated contours [13].
These inputs are passed to second hidden layer in which inputs are corrected and adjusted by weights and then finally passed to outer most layer (output layer) to give outputs (e.g. concentrations).
The weights of the context factors and their text inputs are passed into the recommendation engine and then the content is used for the pre-filtering and the weights are used for the post-filtering computations.The list of Design Patterns results that is produced by the pre-filtering is now reranked based on the weights of importance and thus the utility value of each Design Pattern.
Input data are passed to the correlative neurons and the electrochemical signals are modified by weights.
A histogram of how many input arguments are passed to each method.
This contrasts to the implementations by Zhao et al. [16] and by Ellingson and Baudry [17] where the input chunks are passed to the docking program and read back to the Hadoop framework using local files.
The time series for the selected features are passed as input to the learning algorithms.
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