Hiesinger, Peter Robin,
The self-assembling brain : how neural networks grow smarter / Peter Robin Hiesinger. - 1 online resource (xvi, 364 pages) : illustrations (black and white)
Includes bibliographical references and index.
Frontmatter -- Contents -- Acknowledgments -- Prologue -- The self-assembling brain -- Introduction -- The Present and the Past -- 1 Algorithmic growth -- 2 Of players and rules -- 3 Brain development and artificial intelligence -- Epilogue -- Glossary -- References -- Index
"In this book, Peter Robin Hiesinger explores historical and contemporary attempts to understand the information needed to make biological and artificial neural networks. Developmental neurobiologists and computer scientists with an interest in artificial intelligence - driven by the promise and resources of biomedical research on the one hand, and by the promise and advances of computer technology on the other - are trying to understand the fundamental principles that guide the generation of an intelligent system. Yet, though researchers in these disciplines share a common interest, their perspectives and approaches are often quite different. The book makes the case that "the information problem" underlies both fields, driving the questions that are driving forward the frontiers, and aims to encourage cross-disciplinary communication and understanding, to help both fields make progress. The questions that challenge researchers in these fields include the following. How does genetic information unfold during the years-long process of human brain development, and can this be a short-cut to create human-level artificial intelligence? Is the biological brain just messy hardware that can be improved upon by running learning algorithms in computers? Can artificial intelligence bypass evolutionary programming of "grown" networks? These questions are tightly linked, and answering them requires an understanding of how information unfolds algorithmically to generate functional neural networks. Via a series of closely linked "discussions" (fictional dialogues between researchers in different disciplines) and pedagogical "seminars," the author explores the different challenges facing researchers working on neural networks, their different perspectives and approaches, as well as the common ground and understanding to be found amongst those sharing an interest in the development of biological brains and artificial intelligent systems"--
0691215510 9780691215518
22573/ctv191b91r JSTOR 9519811 IEEE
2020052827
Neural networks (Computer science)
Neural circuitry--Adaptation.
Learning--Physiological aspects.
Artificial intelligence.
Neural Networks, Computer
Artificial Intelligence
R�eseaux neuronaux (Informatique)
R�eseaux nerveux--Adaptation.
Apprentissage--Aspect physiologique.
Intelligence artificielle.
artificial intelligence.
SCIENCE / Life Sciences / Neuroscience
Learning--Physiological aspects.
Artificial intelligence.
Neural circuitry--Adaptation.
Neural networks (Computer science)
Electronic books.
Electronic books.
QA76.87 / .H53 2021
006.3/2
The self-assembling brain : how neural networks grow smarter / Peter Robin Hiesinger. - 1 online resource (xvi, 364 pages) : illustrations (black and white)
Includes bibliographical references and index.
Frontmatter -- Contents -- Acknowledgments -- Prologue -- The self-assembling brain -- Introduction -- The Present and the Past -- 1 Algorithmic growth -- 2 Of players and rules -- 3 Brain development and artificial intelligence -- Epilogue -- Glossary -- References -- Index
"In this book, Peter Robin Hiesinger explores historical and contemporary attempts to understand the information needed to make biological and artificial neural networks. Developmental neurobiologists and computer scientists with an interest in artificial intelligence - driven by the promise and resources of biomedical research on the one hand, and by the promise and advances of computer technology on the other - are trying to understand the fundamental principles that guide the generation of an intelligent system. Yet, though researchers in these disciplines share a common interest, their perspectives and approaches are often quite different. The book makes the case that "the information problem" underlies both fields, driving the questions that are driving forward the frontiers, and aims to encourage cross-disciplinary communication and understanding, to help both fields make progress. The questions that challenge researchers in these fields include the following. How does genetic information unfold during the years-long process of human brain development, and can this be a short-cut to create human-level artificial intelligence? Is the biological brain just messy hardware that can be improved upon by running learning algorithms in computers? Can artificial intelligence bypass evolutionary programming of "grown" networks? These questions are tightly linked, and answering them requires an understanding of how information unfolds algorithmically to generate functional neural networks. Via a series of closely linked "discussions" (fictional dialogues between researchers in different disciplines) and pedagogical "seminars," the author explores the different challenges facing researchers working on neural networks, their different perspectives and approaches, as well as the common ground and understanding to be found amongst those sharing an interest in the development of biological brains and artificial intelligent systems"--
0691215510 9780691215518
22573/ctv191b91r JSTOR 9519811 IEEE
2020052827
Neural networks (Computer science)
Neural circuitry--Adaptation.
Learning--Physiological aspects.
Artificial intelligence.
Neural Networks, Computer
Artificial Intelligence
R�eseaux neuronaux (Informatique)
R�eseaux nerveux--Adaptation.
Apprentissage--Aspect physiologique.
Intelligence artificielle.
artificial intelligence.
SCIENCE / Life Sciences / Neuroscience
Learning--Physiological aspects.
Artificial intelligence.
Neural circuitry--Adaptation.
Neural networks (Computer science)
Electronic books.
Electronic books.
QA76.87 / .H53 2021
006.3/2