BRAIN-INFORMED ARTIFICIAL INTELLIGENCE: RANDOM GRAPHS, DYNAMICAL SYSTEMS AND BEYOND

Abstract

Artificial Intelligence (AI) has made significant progress in recent years, increasingly enabling machines to perform tasks that were once considered unique to human cognition. Despite significant achievements, AI systems have not yet matched the depth of biological intelligence, especially in learning, adaptability, and creativity. Existing neuro-inspired AI models often fall short of fully capturing the complexity and richness of biological intelligence due to oversimplified assumptions about neuro-cognitive processes. This thesis introduces a new AI framework, Brain-Informed AI (BAI), designed to overcome these limitations by embedding more detailed, "brain-informed" inductive bias within AI architectures. BAI is founded on principled Bayesian deep learning models grounded in modern brain science. It comprises two components: a task-specific module for executing various AI tasks, and a brain-informed module for encoding specific neuro-cognitive processes. BAI introduces a new approach to artificial intelligence that emphasizes both ``biological plausibility'' and theoretical soundness. This approach merges brain science insights with theoretically grounded Bayesian machine learning methods, enhancing AI’s reasoning, adaptability, and generative capability.