Biological Learning

Biological learning research aims to understand how brains learn and adapt, seeking to translate these principles into more efficient and robust artificial intelligence. Current efforts focus on developing biologically plausible learning algorithms, such as those inspired by spike-timing-dependent plasticity (STDP) and Hebbian learning, and implementing them in various neural network architectures, including spiking neural networks and vision transformers. This research is significant for both advancing our fundamental understanding of the brain and for creating AI systems that are more energy-efficient, adaptable, and capable of lifelong learning, with applications ranging from robotics to drug discovery.