000			03186nam a2200505 i 4500
001			6267209
003			IEEE
005			20220712204559.0
006			m o d
007			cr |n|||||||||
008			151228s2003 maua ob 001 eng d
010			_z 88038352 (print)
020			_z9780262511506 _qprint
020			_a9780262255592 _qelectronic
020			_a0262011107
035			_a(CaBNVSL)mat06267209
035			_a(IDAMS)0b000064818b417f
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.5 _b.N426 1989eb
082	0	0	_a006.3 _219
245	0	0	_aNeural computing architectures : _bthe design of brain-like machines / _cedited by Igor Aleksander.
250			_a1st MIT Press ed.
264		1	_aCambridge, Massachusetts : _bMIT Press, _c1989.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2003]
300			_a1 PDF (401 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
500			_aIncludes index.
504			_aIncludes bibliographical references (p. 381-393).
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aMcClelland and Rumelhart's Parallel Distributed Processing was the first book to present a definitive account of the newly revived connectionist/neural net paradigm for artificial intelligence and cognitive science. While Neural Computing Architectures addresses the same issues, there is little overlap in the research it reports. These 18 contributions provide a timely and informative overview and synopsis of both pioneering and recent European connectionist research. Several chapters focus on cognitive modeling; however, most of the work covered revolves around abstract neural network theory or engineering applications, bringing important complementary perspectives to currently published work in PDP.In four parts, chapters take up neural computing from the classical perspective, including both foundational and current work; the mathematical perspective (of logic, automata theory, and probability theory), presenting less well-known work in which the neuron is modeled as a logic truth function that can be implemented in a direct way as a silicon read only memory. They present new material both in the form of analytical tools and models and as suggestions for implementation in optical form, and summarize the PDP perspective in a single extended chapter covering PDP theory, application, and speculation in US research. Each part is introduced by the editor.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/28/2015.
650		0	_aNeural computers. _94963
650		0	_aComputer architecture. _93513
655		0	_aElectronic books. _93294
700	1		_aAleksander, Igor. _921509
710	2		_aIEEE Xplore (Online Service), _edistributor. _921510
710	2		_aMIT Press, _epublisher. _921511
776	0	8	_iPrint version _z9780262511506
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267209
942			_cEBK
999			_c72867 _d72867