Cortex Neural Network: learning with Neural Network groups
This addresses the problem of limited multitasking capability in neural networks for AI researchers, though it appears incremental as it builds on existing neural network architectures.
The paper tackles the challenge of enabling neural networks to handle multiple complex cognitive tasks simultaneously, such as processing image and audio inputs together, by proposing the Cortex Neural Network (CrtxNN), which achieves 98.32% accuracy on MNIST and 62% on CIFAR10 concurrently while reducing loss by 40%.
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%.