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. Author manuscript; available in PMC: 2020 May 20.
Published in final edited form as: Cell Rep. 2019 Apr 16;27(3):872–885.e7. doi: 10.1016/j.celrep.2019.03.069

Fig. 2. Widefield image segmentation using an Autoencoder reveals ACX areas with distinct On/Off selectivity.

Fig. 2

(A) Cartoon showing image segmentation. The example image sequence at any time point can be expressed as the weighted summation of ROI 1 and ROI 2 by respective activity level. Our goal of image segmentation is to retrieve activated areas as well as their temporal activation traces. (B) Autoencoder is a neural network with one or more hidden layers between input and output layers, which have the same number of nodes. The weights between input/output layer and hidden layer are adjusted such that the output matches the input as closely as possible. The hidden layer typically has much fewer nodes than input/output layer to achieve dimension reduction. (C) Principle of fitting autoencoder ROIs. Original pixels (left) are linearly combined to produce ROIs (middle) such that each pixel in turn can be approximated (right) by the linear combination of these ROIs, while the weights are interpreted as spatial profile of the ROIs. (D-G) On- and Off-R spatial profiles overlaid with selected autoencoder ROIs to validate ROI placement. (D-G) share the same color scale. (H) Parcellation of ROIs into ACX fields. ROIs outlined in solid lines have the On/Off frequency response areas (FRAs) shown in (I). (I) On/Off-R amplitude is plotted as a function of frequency and sound level for ACX fields. Adjacent blue and red bars represent On/Off-R to the same frequency/sound level combination.