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Fig. 3 Multi-resolution spectrogram patch input classifier and label stream combination overview 2.
Fig. 4 Example of filters obtained after training a 10-filter CNN with 5×5 size filters and a 30 frames long spectrogram patch input.
Patch pipettes were pulled from thick wall borosilicate glass to resistances of 2 3 MΩ, used directly for whole-cell or fire-polished to 7 10 MΩ for single-channel recording (patch input resistance >40 GΩ).
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Espi et al. [8] succeeds in modeling spectrogram patches input as a whole, i.e.; it learns features that describe "globally" a short-time spectrogram patch.
A class-specific predictor takes a low-resolution image patch as input and predicts a corresponding high-resolution patch as output.
A large-scale computational model of a local patch of input layer 4Cα of the primary visual cortex (V1) of the macaque monkey, together with a coarse-grained reduction of the model, are used to understand potential effects of cortical architecture upon neuronal performance.
We used sigmoidal output units, which allow us to interpret each output as the conditional probability that a certain voxel belongs to a cell soma given the original image patch as input.
One common limitation of the above-mentioned methods is that the sequence and structural properties of a surface patch are input to machine-learning methods in the form of vectors.
Both models highlight the superiority of using high-resolution spectrogram patches as input to the models, thanks to DNNs and their ability to model high-dimensional data.
The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet).
Before the image patches are input to CNN, the pixel intensities are normalized to a range of [−1, 1], thus the discriminative power of SUV is lost during the normalization.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com