Stephen Grossberg – författare

How do our brains give rise to our conscious minds? How do our minds learn and consciously experience miraculous creations of our civilizations, such as visual art, music, and the languages whose meanings enable us to think and communicate about our experiences in the world?How does human learning and language differ from some Artificial Intelligence algorithms that fail to explain how human minds work? Unlike these AI algorithms, whose flaws are explained in the book, human brains provide a blueprint for the paradigm of autonomous adaptive intelligence that promises to revolutionize all aspects of society during this century and beyond.This book provides an introductory and self-contained description of exciting answers to these questions that modern theories of mind and brain have proposed. For over 50 years, Stephen Grossberg has been internationally acknowledged to be the most important pioneer and current research leader whose work explains how our brains make our minds. He is often called the Father of AI because he introduced the modern neural networks paradigm in 1957 as a Dartmouth college Freshman, as well as the main equations that help to explain how our brains make our minds. He has also been called the Newton and Einstein of the Mind because he and many gifted collaborators have subsequently developed neural network models of essentially all the main processes whereby our brains make our conscious and unconscious minds in both healthy individuals and clinical patients. Grossberg published his award-winning Magnum Opus, Conscious Mind, Resonant Brain: How Each Brain Makes a Mind, in 2021 to provide a self-contained and non-technical overview and synthesis of these discoveries. The current book describes his recent discoveries about art, music, and meaning in accessible language intended to appeal to all readers who love art and music, and who wonder about the mystery of how languages acquire meaning.

How does your mind work? How does your brain give rise to your mind? These are questions that all of us have wondered about at some point in our lives, if only because everything that we know is experienced in our minds. They are also very hard questions to answer. After all, how can a mind understand itself? How can you understand something as complex as the tool that is being used to understand it? This book provides an introductory and self-contained description of some of the exciting answers to these questions that modern theories of mind and brain have recently proposed. Stephen Grossberg is broadly acknowledged to be the most important pioneer and current research leader who has, for the past 50 years, modelled how brains give rise to minds, notably how neural circuits in multiple brain regions interact together to generate psychological functions. This research has led to a unified understanding of how, where, and why our brains can consciously see, hear, feel, and know about the world, and effectively plan and act within it. The work embodies revolutionary Principia of Mind that clarify how autonomous adaptive intelligence is achieved. It provides mechanistic explanations of multiple mental disorders, including symptoms of Alzheimer's disease, autism, amnesia, and sleep disorders; biological bases of morality and religion, including why our brains are biased towards the good so that values are not purely relative; perplexing aspects of the human condition, including why many decisions are irrational and self-defeating despite evolution's selection of adaptive behaviors; and solutions to large-scale problems in machine learning, technology, and Artificial Intelligence that provide a blueprint for autonomously intelligent algorithms and robots. Because brains embody a universal developmental code, unifying insights also emerge about shared laws that are found in all living cellular tissues, from the most primitive to the most advanced, notably how the laws governing networks of interacting cells support developmental and learning processes in all species. The fundamental brain design principles of complementarity, uncertainty, and resonance that Grossberg has discovered also reflect laws of the physical world with which our brains ceaselessly interact, and which enable our brains to incrementally learn to understand those laws, thereby enabling humans to understand the world scientifically. Accessibly written, and lavishly illustrated, Conscious Mind/Resonant Brain is the magnum opus of one of the most influential scientists of the past 50 years, and will appeal to a broad readership across the sciences and humanities.

Mathematical Psychology and Psychophysiology promotes an understanding of the mind and its neural substrates by applying interdisciplinary approaches to issues concerning behavior and the brain. The contributions present model from many disciplines that share common, conceptual, functional, or mechanistic substrates and summarize recent models and data from neural networks, mathematical genetics, psychoacoustics, olfactory coding, visual perception, measurement, psychophysics, cognitive development, and other areas.The contributors to Mathematical Psychology and Psychophysiology show the conceptual and mathematical interconnectedness of several approaches to the fundamental scientific problem of understanding mind and brain. The book's interdisciplinary approach permits a deeper understanding of theoretical advances as it formally structures a broad overview of the data.

Originally published in 1991, this title was the result of a symposium held at Harvard University. It presents some of the exciting interdisciplinary developments of the time that clarify how animals and people learn to behave adaptively in a rapidly changing environment. The contributors focus on aspects of how recognition learning, reinforcement learning, and motor learning interact to generate adaptive goal-oriented behaviours that can satisfy internal needs – an area of inquiry as important for understanding brain function as it is for designing new types of freely moving autonomous robots. Since the authors agree that a dynamic analysis of system interactions is needed to understand these challenging phenomena – and neural network models provide a natural framework for representing and analysing such interactions – all the articles either develop neural network models or provide biological constraints for guiding and testing their design.