Neural Networks and Applications

Neural networks are computational systems loosely inspired by the brain's architecture, built from layers of interconnected nodes that learn to recognize patterns, approximate functions, and make predictions by adjusting the strength of their connections through processes like backpropagation. Different architectures serve different purposes: feedforward networks map inputs to outputs in a single pass, recurrent networks handle sequential data by maintaining a form of memory, self-organizing maps compress high-dimensional data into interpretable structures, and deep learning stacks many layers to extract increasingly abstract representations. These methods now underpin advances in speech recognition, medical diagnosis, autonomous systems, and scientific modeling, making questions about how and why they work theoretically important as well as practically urgent. Active research focuses on understanding why deep networks generalize despite having far more parameters than training examples, how to make them robust against adversarial inputs, and whether architectures like radial basis function networks or hybrid models can offer better efficiency or interpretability than standard deep learning approaches.

Works

253,169

Total citations

4,465,493

Keywords

Neural NetworksSelf-Organizing MapsBackpropagation LearningRadial Basis Function NetworksDeep LearningArtificial Neural Networks