Toward Autonomous Adaptive Intelligence: Building Upon Neural Models of How Brains Make Minds

This article surveys the development of mathematical laws, circuits, and architectures that model how our brains make our minds, and shows how these contributions provide a blueprint for developing general-purpose autonomous adaptive algorithms and robots for intelligent applications in engineering and technology. The mathematical laws for short-term memory, medium-term memory, and long-term memory that provide the contemporary foundation for all subsequent biological neural network models were published in 1968, followed by a steady stream of model discovery and development to the present time. The Cohen-Grossberg model and theorem that was published in this journal in 1983 was one step in this series of developments. It proved global limit theorems for a general class of neural networks using a Lyapunov function as one tool. These theorems provide a guarantee that learning in these networks generates stable memories. This article surveys additional mathematical foundations for neural network design and applications and describes a modeling method for the incremental discovery of models with increasingly powerful and general functional capabilities. This method is illustrated by the adaptive resonance theory, or ART, architectures which explain how our brains autonomously learn to attend, recognize, and predict objects and events in a changing world, along the way explaining how our brains become conscious, as well as the computational constraints on learning that forced evolution to discover conscious states of mind. Multiple types of resonances support different kinds of conscious awareness and enable the explanation and prediction of large psychological and neurobiological databases about perception, cognition, emotion, and action. Because ART can be derived from a thought experiment about a universal problem of error correction in a changing world, its expanding applications to the development of autonomously intelligent systems should transform future technologies.