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008201123t20212021nju ob 001 0 eng
010 ▼a 2020052827
020 ▼a 0691215510 ▼q electronic book
020 ▼a 9780691215518 ▼q (electronic bk.)
020 ▼z 9780691181226 ▼q hardcover
035 ▼a 2667086 ▼b (N$T)
035 ▼a (OCoLC)1224585570
037 ▼a 22573/ctv191b91r ▼b JSTOR
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d OCLCO ▼d OCLCF ▼d EBLCP ▼d UKAHL ▼d JSTOR ▼d YDX ▼d MRB ▼d N$T ▼d 248032
042 ▼a pcc
049 ▼a MAIN
05000 ▼a QA76.87 ▼b .H53 2021
072 7 ▼a SCI ▼x 089000 ▼2 bisacsh
072 7 ▼a SCI ▼x 090000 ▼2 bisacsh
072 7 ▼a SCI ▼x 009000 ▼2 bisacsh
072 7 ▼a PSY ▼x 051000 ▼2 bisacsh
072 7 ▼a COM ▼x 004000 ▼2 bisacsh
08200 ▼a 006.3/2 ▼2 23
1001 ▼a Hiesinger, Peter Robin, ▼e author.
24514 ▼a The self-assembling brain : ▼b how neural networks grow smarter / ▼c Peter Robin Hiesinger. ▼h [electronic resource]
260 ▼a Princeton : ▼b Princeton University Press, ▼c [2021]
300 ▼a 1 online resource (xvi, 364 pages)
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b n ▼2 rdamedia
338 ▼a online resource ▼b nc ▼2 rdacarrier
504 ▼a Includes bibliographical references and index.
520 ▼a "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"-- ▼c Provided by publisher.
588 ▼a Description based on online resource; title from digital title page (viewed on May 11, 2021).
590 ▼a Added to collection customer.56279.3
650 0 ▼a Neural networks (Computer science)
650 0 ▼a Neural circuitry ▼x Adaptation.
650 0 ▼a Learning ▼x Physiological aspects.
650 0 ▼a Artificial intelligence.
650 7 ▼a Artificial intelligence. ▼2 fast ▼0 (OCoLC)fst00817247
650 7 ▼a Neural circuitry ▼x Adaptation. ▼2 fast ▼0 (OCoLC)fst01036246
650 7 ▼a Neural networks (Computer science) ▼2 fast ▼0 (OCoLC)fst01036260
650 7 ▼a SCIENCE / Life Sciences / Neuroscience ▼2 bisacsh
655 4 ▼a Electronic books.
77608 ▼i Print version: ▼a Hiesinger, Peter Robin. ▼t The self-assembling brain ▼d Princeton : Princeton University Press, [2021] ▼z 9780691181226 ▼w (DLC) 2020052826
85640 ▼3 EBSCOhost ▼u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2667086
938 ▼a Askews and Holts Library Services ▼b ASKH ▼n AH38316545
938 ▼a ProQuest Ebook Central ▼b EBLB ▼n EBL6483050
938 ▼a EBSCOhost ▼b EBSC ▼n 2667086
990 ▼a 관리자
994 ▼a 92 ▼b N$T