Self-Assembling Brain (eBook)

How Neural Networks Grow Smarter

Peter Robin Hiesinger (Autor)

eBook Download: PDF | EPUB

2021
384 Seiten
Princeton University Press (Verlag)
978-0-691-21551-8 (ISBN)

Infos

What neurobiology and artificial intelligence tell us about how the brain builds itself How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network?As Peter Robin Hiesinger argues, "e;the information problem"e; underlies both fields, motivating the questions driving forward the frontiers of research. How does genetic information unfold during the years-long process of human brain development-and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of "e;grown"e; networks? Through a series of fictional discussions between researchers across disciplines, complemented by in-depth seminars, Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives and approaches, as well as the common ground shared by those interested in the development of biological brains and AI systems. In the end, Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.Written for readers interested in advances in neuroscience and artificial intelligence, The Self-Assembling Brain looks at how neural networks grow smarter.

Produkt-Details

• Erscheint lt. Verlag: 4.5.2021
• Zusatzinfo: 49 b/w illus.
• Sprache: englisch
• Themenwelt: Geisteswissenschaften ► Psychologie ► Allgemeine Psychologie
• Themenwelt: Geisteswissenschaften ► Psychologie ► Biopsychologie / Neurowissenschaften
• Themenwelt: Geisteswissenschaften ► Psychologie ► Verhaltenstherapie
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Naturwissenschaften ► Biologie ► Humanbiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Zoologie
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Angewandte Physik
• Schlagworte: Action potential • Addition • algorithm • algorithmic growth • Algorithmic information theory • Allen Newell • Analogy • artificial general intelligence • Artificial Life • Artificial Neural Network • Artificial neuron • Autonomous Agent • axon • Axon Guidance • backpropagation • Behavior • Biological neural network • biologist • Biology • Bit • brain • Brain Development • brain wiring • Calculation • cell membrane • Cell surface receptor • cell type • Cellular Automaton • chemical synapse • Cognitive Bias • Complexity • Computation • Computer • computer intelligence • connectome • convolutional neural network • cortical column • Cyberneticist • Cybernetics • Deep learning • Developmental Biology • Enzyme • Equipotentiality • Evolution • Evolutionary Programming • filopodia • Gary Macus • Gene • Gene product • genetic code • geneticist • Growth cone • Growth factor • guidance cue • How to Create a Mind • Human Brain • Inception • Information Theory • interaction • Iteration • learning rule • Lecture • machine learning • Memory • Metabolism • Molecular Biology • Molecule • Mutant • Mutation • Natural selection • Neocortex • Netrin • Neural Circuit • Neurogenetics • Neuromodulation • Neuron • Neuroscience • neuroscientist • Neurotransmission • Nucleic acid sequence • organism • Penetrance • Perceptron • Peter Sterling • Phenotype • Prediction • Principles of Neural Design • Probability • Processing (programming language) • Proportion (architecture) • Protein • Ray Kurzweil • Ray Solomonoff • Receptor (biochemistry) • result • Reticular theory • retina • Robotics • Roger Sperry • Rule 110 • Schematic • Scientist • Self-Assembly • Self-Organization • Seymour Benzer • Simon Laughlin • Simulation • Sydney Brenner • Synapse • Synapsis • synaptic weight • Technology • The Birth of the Mind • Thought • To this Day • Transcription factor • Unsupervised Learning • Vertebrate • Wiring (development platform) • Wiring diagram
• ISBN-10: 0-691-21551-0 / 0691215510
• ISBN-13: 978-0-691-21551-8 / 9780691215518