Hemisphere Neural Network

Hemisphere neural networks (HNNs) are a class of neural network architectures designed to improve model performance and interpretability by dividing the network into specialized "hemispheres." Current research focuses on developing HNNs for specific tasks, such as macroeconomic forecasting (where separate hemispheres model mean and variance) and brain activity prediction (where hemispheres process data from different brain regions). These architectures address challenges like overfitting and indeterminacy in complex models, leading to improved accuracy and reliability in various applications. The development and application of HNNs represent a significant advancement in neural network design, offering potential for enhanced performance and interpretability across diverse fields.