Convolutional neural networks (CNNs) are inspired by biological processes in the visual cortex and contain simple and complex cells that detect visual features. CNNs use convolution layers to extract low level features from input data and pooling layers to combine similar features into higher level representations. Fully connected neural networks differ from CNNs by not taking spatial data as input.

7. (simple cell) (complex cell)
convolution pooling

8. (simple cell)
low level feature
(complex cell)
high level feature

9. Fully connected NN CNN

24. (simple cell) (complex cell)
convolution layer

27. (simple cell) (complex cell)
Pooling layer