Information Cohomology and Probabilistic Topos for Consciousness Modeling - From Elementary Perception to Machine Learning
Offered By: Models of Consciousness Conferences via YouTube
Course Description
Overview
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.
Syllabus
Pierre Baudot - Information cohomology and probabilistic topos for consciousness modeling
Taught by
Models of Consciousness Conferences
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent