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Supplementary Fig. 3 Whole-brain mapping of input to the mPFC.
This structured bottleneck layer could be treated as a nonlinear mapping of input features.
Depending on the chosen entropy coding scheme, there is not necessarily a one-to-one mapping of input symbols to codes.
Other than these methods, non-linear transfer function-based mapping of input to output intensities can provide an increase in image contrast and remove uneven brightness distribution [27, 28].
Mapping of input sequences to logical sequences.
Here Φ(X) represents the mapping of input data X into the kernel space.
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(e) Distribution of the starter neurons (expressing V5 and RV-eGFP) within the mPFC subregions in the animals (n = 17) included in the whole-brain mapping of inputs.
(f) Boxplot of the proportion starter neurons detected within the mPFC in the animals (n = 17) included in the whole-brain mapping of inputs.
The mapping of inputs to outputs also shows an incremental linear progression, suggesting its implementation in a range of physical systems.
Input selection for data-driven rainfall-runoff models is an important task as these models find the relationship between rainfall and runoff by direct mapping of inputs to output.
In the inference stage, a set of 'IF-THEN' rules defines the mapping of inputs to output.
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