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How a Brain Makes a Mind

A historical overview is given of interdisciplinary work in physics and psychology by some of the greatest nineteenth-century scientists, and why the fields split, leading to a century of ferment before the current scientific revolution in mind-brain sciences began to understand how we autonomously adapt to a changing world. New nonlinear, nonlocal, and nonstationary intuitions and laws are needed to understand how brains make minds. Work of Helmholtz on vision illustrates why he left psychology. His concept of unconscious inference presaged modern ideas about learning, expectation, and matching that this book scientifically explains. The fact that brains are designed to control behavioral success has profound implications for the methods and models that can unify mind and brain. Backward learning in time, and serial learning, illustrate why neural networks are a natural language for explaining brain dynamics, including the correct functional stimuli and laws for short-term memory (STM), medium-term memory (MTM), and long-term memory (LTM) traces. In particular, brains process spatial patterns of STM and LTM, not just individual traces. A thought experiment leads to universal laws for how neurons, and more generally all cellular tissues, process distributed STM patterns in cooperative-competitive networks without experiencing contamination by noise or pattern saturation. The chapter illustrates how thinking this way leads to unified and principled explanations of huge databases. A brief history of the advantages and disadvantages of the binary, linear, and continuous-nonlinear sources of neural models is described, and how models like Deep Learning and the author’s contributions fit into it.