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_c200425769 _d43692 |
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| 001 | 12639266 | ||
| 003 | TR-AnTOB | ||
| 005 | 20190322123532.0 | ||
| 006 | m o d | ||
| 007 | cr nn 008mamaa | ||
| 008 | 151106s2016 gw | o |||| 0|eng d | ||
| 020 | _a9783319251158 | ||
| 024 | 7 |
_a10.1007/978-3-319-25115-8 _2doi |
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| 035 | _a(DE-He213)978-3-319-25115-8 | ||
| 035 | _a12639266 | ||
| 040 |
_aTR-AnTOB _beng _cTR-AnTOB _erda |
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| 041 | _aeng | ||
| 050 | 4 |
_aQ172.5 _b.S96 2016 |
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| 090 |
_aQ172.5 _b.S96 2016 |
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| 100 | 1 | _aLehnert, Judith. | |
| 245 | 1 | 0 |
_aControlling Synchronization Patterns in Complex Networks _h[electronic resource] / _cby Judith Lehnert. |
| 250 | _a1st ed. 2016. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016. |
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| 300 |
_aXV, 203 p. 67 illus., 50 illus. in color : _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 0 |
_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5053 |
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| 505 | 0 | _aIntroduction -- Complex Dynamical Networks -- Synchronization In Complex Networks -- Control of Synchronization Transitions by Balancing Excitatory and Inhibitory Coupling -- Cluster and Group Synchrony: The Theory -- Zero-Lag and Cluster Synchrony: Towards Applications -- Adaptive Control -- Adaptive Time-Delayed Feedback Control -- Adaptive Control of Cluster States in Network Motifs -- Adaptive Topologies -- Conclusion. | |
| 506 | _aAccess restricted by licensing agreement. | ||
| 520 | _aThis research aims to achieve a fundamental understanding of synchronization and its interplay with the topology of complex networks. Synchronization is a ubiquitous phenomenon observed in different contexts in physics, chemistry, biology, medicine and engineering. Most prominently, synchronization takes place in the brain, where it is associated with several cognitive capacities but is - in abundance - a characteristic of neurological diseases. Besides zero-lag synchrony, group and cluster states are considered, enabling a description and study of complex synchronization patterns within the presented theory. Adaptive control methods are developed, which allow the control of synchronization in scenarios where parameters drift or are unknown. These methods are, therefore, of particular interest for experimental setups or technological applications. The theoretical framework is demonstrated on generic models, coupled chemical oscillators and several detailed examples of neural networks. | ||
| 590 | _aAccess is available to the Yale community. | ||
| 650 | 0 | _aPhysics. | |
| 650 | 0 | _aChemistry, Physical and theoretical. | |
| 650 | 0 | _aSystem theory. | |
| 650 | 0 | _aNeural networks (Computer science) | |
| 650 | 0 | _aVibration. | |
| 650 | 0 | _aDynamics. | |
| 710 | 2 | _aSpringerLink (Online service) | |
| 730 | 0 | _aSpringer ebooks. | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319251134 |
| 856 | 4 | 0 |
_yOnline book _uhttp://dx.doi.org/10.1007/978-3-319-25115-8 |
| 942 |
_2lcc _cBK |
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