Intelligence Emerging

Intelligence Emerging

Adaptivity and Search in Evolving Neural Systems

By Keith L. Downing

An investigation of intelligence as an emergent phenomenon, integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence.
Hardcover $56.00 S £46.00
Paperback $19.75 S £15.99





An investigation of intelligence as an emergent phenomenon, integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence.

Emergence—the formation of global patterns from solely local interactions—is a frequent and fascinating theme in the scientific literature both popular and academic. In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on neural networks, both natural and artificial, and how their adaptability in three time frames—phylogenetic (evolutionary), ontogenetic (developmental), and epigenetic (lifetime learning)—underlie the emergence of cognition. Integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence, Downing provides a series of concrete examples of neurocognitive emergence. Doing so, he offers a new motivation for the expanded use of bio-inspired concepts in artificial intelligence (AI), in the subfield known as Bio-AI.

One of Downing's central claims is that two key concepts from traditional AI, search and representation, are key to understanding emergent intelligence as well. He first offers introductory chapters on five core concepts: emergent phenomena, formal search processes, representational issues in Bio-AI, artificial neural networks (ANNs), and evolutionary algorithms (EAs). Intermediate chapters delve deeper into search, representation, and emergence in ANNs, EAs, and evolving brains. Finally, advanced chapters on evolving artificial neural networks and information-theoretic approaches to assessing emergence in neural systems synthesize earlier topics to provide some perspective, predictions, and pointers for the future of Bio-AI.


Out of Print ISBN: 9780262029131 504 pp. | 7 in x 9 in 201 b&w illus.


$19.75 S | £15.99 ISBN: 9780262536844 504 pp. | 7 in x 9 in 201 b&w illus.


  • In this deep and broad book, Downing takes up the challenge of explaining how intelligence emerges, in the evolutionary, developmental, and learning senses. Drawing together evidence from neuroscience, computational neuroscience, classical AI, and connectionism, Downing focuses on search and representation as defining activities of intelligence, and as critical prerequisites to emergence in both the human brain and in computational models. This is a detailed and rich approach that will serve as an example of careful thought and scholarship in this area. Moreover, Downing's work will provide a strong technical foundation for continued scientific analysis of the emergence of intelligence.

    Jeff Shrager

    Senior Fellow, CommerceNet; Consulting Professor of Symbolic Systems, Stanford University

  • Early on in the book Downing formulates his ambition: to bring emergence to the forefront and to keep it there. The result is an impressively wide-ranging and engaging exploration of complex adaptive systems in three time frames: evolution, development, and learning. I warmly recommend this book to anyone with a serious interest in embodied cognition and bio-inspired artificial intelligence.

    Tom Ziemke

    Professor of Cognitive Science, University of Skövde and Linköping University, Sweden

  • In this refreshing and forward-thinking exploration of natural and artificial intelligence, Keith Downing focuses on the emergence of adaptive complexity in learning, development, and evolution. While the discussion is rooted in traditional concepts of search and representation, the book charts an ambitious path for biologically inspired artificial intelligence in the future.

    Lee Spector

    Professor of Computer Science, Hampshire College; author of Automatic Quantum Computer Programming: A Genetic Programming Approach; Editor-in-chief of Genetic Programming and Evolvable Machines