Frank Rosenblatt’s 1958 paper introduced the perceptron, a probabilistic model mimicking neural connections for learning and pattern recognition, laying the mathematical and conceptual groundwork for modern neural networks and sparking decades of research in artificial intelligence, despite its early limitations and later critiques.
If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions:
How is information about the physical world sensed, or detected, by the biological system?
In what form is information stored, or remembered?
How does information contained in storage, or in memory, influence recognition and behavior?
This paper proposes a quantitative framework for the rise-and-fall trajectory of complexity in closed systems, showing that a coffee-and-cream cellular automaton exhibits a bell-curve of apparent complexity when particles interact, thereby linking information theory with thermodynamics and self-organization.
