Inspired Learning

Inspired learning aims to develop more biologically plausible machine learning algorithms by drawing inspiration from the brain's learning mechanisms, addressing limitations of current deep learning methods like backpropagation. Current research focuses on developing and refining neurobiologically-inspired credit assignment algorithms, including predictive coding and models based on Hebbian learning and dopamine modulation, often implemented within spiking neural networks or self-organizing map architectures. This approach promises more energy-efficient, adaptable, and potentially more robust AI systems, with applications ranging from neuromorphic computing to improved signal processing and data reconstruction.