MARC details
| 000 -LEADER |
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| fixed length control field |
02603 a2200229 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230925b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780691241692 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.32 HIE |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Hiesinger, Peter Robin |
| 245 ## - TITLE STATEMENT |
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| Title |
Self-assembling brain: how neural networks grow smarter |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Princeton, New Jersey: |
| Name of publisher, distributor, etc |
Princeton University Press, |
| Date of publication, distribution, etc |
2021. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiii, 346p.: |
| Other physical details |
ill.; pbk.: |
| Dimensions |
23cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes Epilogue, Glossary, References and Index |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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?<br/><br/>As Peter Robin Hiesinger argues, “the information problem” 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 “grown” 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.<br/><br/>Written for readers interested in advances in neuroscience and artificial intelligence, The Self-Assembling Brain looks at how neural networks grow smarter.<br/><br/>https://press.princeton.edu/books/hardcover/9780691181226/the-self-assembling-brain |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Neural networks |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial Intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Neural circuitry |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Psychological aspects |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer Science |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Biomedical researach |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
