Information Cohomology and Probabilistic Topos for Consciousness Modeling - From Elementary Perception to Machine Learning

Explore a comprehensive lecture on information cohomology and probabilistic topos for modeling consciousness. Delve into the intersection of biology, neuroscience, physics, and mathematics through a theory that extends information theory within algebraic topology. Examine the concept of information structures in n-body interacting systems, interpreted through a Leibnizian monadic-panpsychic framework. Investigate the electrodynamic nature of consciousness and its analogical code, supported by neuroscience and psychophysics findings. Discover how this approach accounts for diverse learning mechanisms, including adaptive and homeostatic processes across multiple scales. Learn about the axiomatization and logic of cognition rooted in measure theory, expressed through a topos intrinsic probabilistic constructive logic. Understand how information topology synthesizes major consciousness models within a formal Gestalt theory, connecting information structures to Galois cohomology and symmetries. Explore practical applications of information topology in AI and machine learning recognition challenges.