000 04302nam a22005175i 4500
001 978-3-642-00189-5
003 DE-He213
005 20140220084521.0
007 cr nn 008mamaa
008 100403s2010 gw | s |||| 0|eng d
020 _a9783642001895
_9978-3-642-00189-5
024 7 _a10.1007/978-3-642-00189-5
_2doi
050 4 _aQC174.7-175.36
072 7 _aPHS
_2bicssc
072 7 _aPHDT
_2bicssc
072 7 _aSCI055000
_2bisacsh
082 0 4 _a621
_223
100 1 _aRamacher, Ulrich.
_eauthor.
245 1 0 _aOn the Construction of Artificial Brains
_h[electronic resource] /
_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 _uhttp://dx.doi.org/10.1007/978-3-642-00189-5
912 _aZDB-2-PHA
999 _c111307
_d111307