000			04257nam a22004935i 4500
001			u373327
003			SIRSI
005			20160812084141.0
007			cr nn 008mamaa
008			100403s2010 gw | s |||| 0|eng d
020			_a9783642001895 _9978-3-642-00189-5
040			_cMX-MeUAM
050		4	_aQC174.7-175.36
082	0	4	_a621 _223
100	1		_aRamacher, Ulrich. _eauthor.
245	1	0	_aOn the Construction of Artificial Brains _h[recurso electrónico] / _cby Ulrich Ramacher, Christoph Malsburg.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aApprox. 370 p. 142 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPrologue -- The Difficulty of Modelling Artificial Brains -- Information Processing in Nets with Constant Synapses -- Theory of Nets with Constant or Dynamic Synapses -- Macro-Dynamics of Nets with Constant Synapses -- Information Processing with Dynamic Synapses -- Nets for Feature Detection -- Nets for Feature Recognition -- Nets for Robust Head Detection -- Extensions of the Vision Architecture -- Look-out -- Preliminary Considerations on the Microelectronic Implementation -- Elementary Circuits for Neurons, Synapses, and Photosensors -- Simulation of Microelectronic Neural Circuits and Systems -- Architecture and Chip Design of the Feature Recognizer -- Architecture and Chip Design of the Feature Detector -- 3D Stacking Technology -- Architecture of First Generation Vision Cube -- End.
520			_aThis book presents a first generation of artificial brains, using vision as sample application. An object recognition system is built, using neurons and synapses as exclusive building elements. The system contains a feature pyramid with 8 orientations and 5 resolution levels for 1000 objects and networks for binding of features into objects. This vision system can recognize objects robustly in the presence of changes in illumination, deformation, distance and pose (as long as object components remain visible). The neuro-synaptic network owes its functional power to the introduction of rapidly modifiable dynamic synapses. These give a network greater pattern recognition capabilities than are achievable with fixed connections. The spatio-temporal correlation structure of patterns is captured by a single synaptic differential equation in a universal way. The correlation can appear as synchronous neural firing, which signals the presence of a feature in a robust way, or binds features into objects. Although in this book we can present only a first generation artificial brain and believe many more generations will have to follow to reach the full power of the human brain, we nevertheless see a new era of computation on the horizon. There were times when computers, with their precision, reliability and blinding speed, were considered to be as superior to the wet matter of our brain as a jet plane is to a sparrow. These times seem to be over, given the fact that digital systems inspired by formal logic and controlled algorithmically - today's computers - are hitting a complexity crisis. A paradigm change is in the air: from the externally organised to the self-organised computer, of which the results described in this book may give an inkling.
650		0	_aPhysics.
650		0	_aNeurosciences.
650		0	_aArtificial intelligence.
650		0	_aVibration.
650		0	_aElectronics.
650	1	4	_aPhysics.
650	2	4	_aStatistical Physics, Dynamical Systems and Complexity.
650	2	4	_aVibration, Dynamical Systems, Control.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aNeurosciences.
650	2	4	_aElectronics and Microelectronics, Instrumentation.
700	1		_aMalsburg, Christoph. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642001888
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-00189-5
596			_a19
942			_cLIBRO_ELEC
999			_c201207 _d201207