Modular Neural

Modular neural networks aim to improve efficiency, robustness, and generalization by decomposing complex tasks into smaller, interconnected modules. Current research focuses on developing effective training methods, such as module-to-module knowledge distillation, and exploring various architectures including Neural Attentive Circuits and Mixture-of-Experts models, often inspired by biological neural networks. This approach offers advantages in resource-constrained environments and allows for easier integration of control techniques for enhanced stability and performance, impacting fields like neuromorphic computing and reinforcement learning.