Convolutional Neural Network with Biologically Inspired Retinal Structure

In this paper, we propose a new Convolutional Neural Network (CNN) with biologically inspired retinal structure and ON/OFF Rectified Linear Unit (ON/OFF ReLU). Retinal structure enhances input images by center surround difference of green-red and blue-yellow components, which in turn creates positive as well as negative features like ON/OFF visual pathway of retina to make a total of 12 feature channels. This ON/OFF concept is also adopted to each convolutional layer of CNN. We prefer to call this ON/OFF ReLU. In contrast, conventional ReLU passes only positive features of each convolutional layer and may loose important information from negative features. Moreover, it also happens to loose learning chance if results are saturated to zero. However, in our proposed model, we use both positive and negative information, which provides a possibility to learn through negative results. We also present the experimental results conducted on CIFAR-10 dataset and atrial fibrillation prediction for health monitoring, and show how effectively the negative information and retinal structure improves the performance of conventional CNN.