| 000 | 03462nam a22005895i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-59042-0 | ||
| 003 | DE-He213 | ||
| 005 | 20220801214626.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 201106s2021 sz | s |||| 0|eng d | ||
| 020 |
_a9783030590420 _9978-3-030-59042-0 |
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| 024 | 7 |
_a10.1007/978-3-030-59042-0 _2doi |
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| 050 | 4 | _aTA167.5 | |
| 072 | 7 |
_aMQW _2bicssc |
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_aTEC059000 _2bisacsh |
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_aMQW _2thema |
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| 082 | 0 | 4 |
_a610.28 _223 |
| 100 | 1 |
_aPastore, Vito Paolo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _939458 |
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| 245 | 1 | 0 |
_aEstimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies _h[electronic resource] : _bStatistical and Computational Methods / _cby Vito Paolo Pastore. |
| 250 | _a1st ed. 2021. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
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| 300 |
_aXV, 87 p. 43 illus., 39 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 | 1 |
_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5061 |
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| 505 | 0 | _aIntroduction -- Materials and Methods -- Results -- Conclusion. | |
| 520 | _aThis book describes a set of novel statistical algorithms designed to infer functional connectivity of large-scale neural assemblies. The algorithms are developed with the aim of maximizing computational accuracy and efficiency, while faithfully reconstructing both the inhibitory and excitatory functional links. The book reports on statistical methods to compute the most significant functional connectivity graph, and shows how to use graph theory to extract the topological features of the computed network. A particular feature is that the methods used and extended at the purpose of this work are reported in a fairly completed, yet concise manner, together with the necessary mathematical fundamentals and explanations to understand their application. Furthermore, all these methods have been embedded in the user-friendly open source software named SpiCoDyn, which is also introduced here. All in all, this book provides researchers and graduate students in bioengineering, neurophysiology and computer science, with a set of simplified and reduced models for studying functional connectivity in in silico biological neuronal networks, thus overcoming the complexity of brain circuits. . | ||
| 650 | 0 |
_aNeurotechnology (Bioengineering). _936420 |
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| 650 | 0 |
_aDynamics. _939459 |
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| 650 | 0 |
_aNonlinear theories. _93339 |
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| 650 | 0 |
_aCoding theory. _94154 |
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| 650 | 0 |
_aInformation theory. _914256 |
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| 650 | 0 |
_aGraph theory. _93662 |
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| 650 | 1 | 4 |
_aNeuroengineering. _936423 |
| 650 | 2 | 4 |
_aApplied Dynamical Systems. _932005 |
| 650 | 2 | 4 |
_aCoding and Information Theory. _939460 |
| 650 | 2 | 4 |
_aGraph Theory. _93662 |
| 710 | 2 |
_aSpringerLink (Online service) _939461 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030590413 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030590437 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030590444 |
| 830 | 0 |
_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5061 _939462 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-59042-0 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c76561 _d76561 |
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