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Abstract
We propose a system that integrates a convolutional neural network with bio-inspired features to exploit strengths of them, evaluate the performance of this integration and experimentations for providing better performance than the traditional convolutional neural networks. Our experiments trained on two datasets, CIFAR-10 and ImageNet, the largest dataset with high-resolution images. We run experiments over different GPUs, with different performances, like GT 740M and GTX 980. Furthermore, we provide a review study for the methods used for object recognition in the last decades until today and analysis study for some particular systems. The review study covers the traditional systems, the deep learning systems, and the cortical models, which are bio-inspired systems based on neuroscience experiments and researches to imitate the human visual systems