Figure 1. Deep neural network architecture and properties.

(a) The DNN architecture comprised 8 layers. Each of layers 1–5 contained a combination of convolution, max-pooling and normalization stages, whereas the last three layers were fully connected. The DNN takes pixel values as inputs and propagates information feed-forward through the layers, activating model neurons with particular activation values successively at each layer. (b) Visualization of example DNN connections. The thickness of highlighted lines (colored to ease visualization) indicates the weight of the strongest connections going in and out of neurons, starting from a sample neuron in layer 1. Neurons in layer 1 are represented by their filters, and in layers 2–5 by gray dots. For combined visualization of connections between neurons and neuron RF selectivity please visit http://brainmodels.csail.mit.edu/dnn/drawCNN/.