  Neural Network has been successfully applied to many real-world problems,
such as image recognition and machine translation. However, for the current
architecture of neural networks, it is hard to perform complex cognitive tasks,
for example, to process the image and audio inputs together. Cortex, as an
important architecture in the brain, is important for animals to perform the
complex cognitive task. We view the architecture of Cortex in the brain as a
missing part in the design of the current artificial neural network. In this
paper, we purpose Cortex Neural Network (CrtxNN). The Cortex Neural Network is
an upper architecture of neural networks which motivated from cerebral cortex
in the brain to handle different tasks in the same learning system. It is able
to identify different tasks and solve them with different methods. In our
implementation, the Cortex Neural Network is able to process different
cognitive tasks and perform reflection to get a higher accuracy. We provide a
series of experiments to examine the capability of the cortex architecture on
traditional neural networks. Our experiments proved its ability on the Cortex
Neural Network can reach accuracy by 98.32% on MNIST and 62% on CIFAR10 at the
same time, which can promisingly reduce the loss by 40%.
