Leveraging dendritic properties to advance machine learning and neuro-inspired computing

• DOI: 10.1016/j.conb.2024.102853
• Publication Date: 2024-02-22T20:51:51Z
• Authors: Michalis Pagkalos; Roman Makarov; Panayiota Poirazi
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Gastropoda; Computer Science - Neural and Evolutionary Computing; Brain; 7. Clean energy; Machine Learning (cs.LG); Machine Learning; Neurology; Artificial Intelligence; Quantitative Biology - Neurons and Cognition; FOS: Biological sciences; Animals; Neurons and Cognition (q-bio.NC); Neural and Evolutionary Computing (cs.NE)

The brain is a remarkably capable and efficient system. It can process store huge amounts of noisy unstructured information using minimal energy. In contrast, current artificial intelligence (AI) systems require vast resources for training while still struggling to compete in tasks that are trivial biological agents. Thus, brain-inspired engineering has emerged as promising new avenue designing sustainable, next-generation AI systems. Here, we describe how dendritic mechanisms neurons have inspired innovative solutions significant problems, including credit assignment multilayer networks, catastrophic forgetting, high energy consumption. These findings provide exciting alternatives existing architectures, showing research pave the way building more powerful energy-efficient learning