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Research Topics in Hopfield Neural Networks

Research Topics in Hopfield Neural Networks

  Hopfield neural network (HNN) is the well-known type of artificial neural network that represents a new neural computational model and comprises fully interconnected neural networks. The computation of fully connected recurrent neurons is based on a converging interactive process that generates a different response than our normal neural networks. HNN belongs to the type of recurrent neural network. The importance of the HNN model is to address and solve optimization problems through the highly interconnected neurons. HNN is significantly used in performing auto association and optimization tasks.
  HNN provides better performance and robustness than other neural networks. Hopfield neural networks are classified as discrete Hopfield and continuous Hopfield. It delivers better performance and robustness than other neural networks.HNN owns great potential in the applications of life science and engineering, such as associating memory, medical imaging, information storage, cognitive study, and supervised learning. Some of the specific applications of HNN are pattern recognition, image detection, and recognition, enhancement of X-Ray images, medical image restoration, and many more. Recent developments in HNN are modern Hopfield neural networks or dense associative memory, complex-valued Hopfield neural networks, quantum Hopfield neural networks, and delayed Hopfield neural networks.