A Biologically Inspired Deep CNN Model

Recently, the Deep Convolutional Neural Networks (DCNN) have achieved state-of-the-art performances with many tasks in image and video analysis. However, it is a very challenging problem to devise a good DCNN model as there are so many choices to be made by a network designer, including the depth, the number of feature maps, interconnection patterns, window sizes for convolution and pooling layers, etc. These choices constitute a huge search space that makes it impractical to discover an optimal network structure with any systematic approaches. In this paper, we strive to develop a good DCNN model by borrowing biological guidance from the human visual cortex. By making an analogy between the proposed DCNN model and the human visual cortex, many critical design choices of the proposed model can be determined with some simple calculations. Comprehensive experimental evaluations demonstrate that the proposed DCNN model achieves state-of-the-art performances on four widely used benchmark datasets: CIFAR-10, CIFAR-100, SVHN and MNIST.