001432842 000__ 03664cam\a2200613\i\4500 001432842 001__ 1432842 001432842 003__ OCoLC 001432842 005__ 20230309003536.0 001432842 006__ m\\\\\o\\d\\\\\\\\ 001432842 007__ cr\nn\nnnunnun 001432842 008__ 201106s2021\\\\sz\a\\\\ob\\\\000\0\eng\d 001432842 019__ $$a1204207254$$a1206401586$$a1288017965$$a1288158984 001432842 020__ $$a9783030590420$$q(electronic book) 001432842 020__ $$a3030590429$$q(electronic book) 001432842 020__ $$z3030590410 001432842 020__ $$z9783030590413 001432842 0247_ $$a10.1007/978-3-030-59042-0$$2doi 001432842 035__ $$aSP(OCoLC)1225563623 001432842 040__ $$aSFB$$beng$$erda$$epn$$cSFB$$dOCLCO$$dYDXIT$$dOCLCF$$dGW5XE$$dOCLCO$$dEBLCP$$dYDX$$dOCLCO$$dGPRCL$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCQ 001432842 049__ $$aISEA 001432842 050_4 $$aQP363.3 001432842 08204 $$a612.8/2$$223 001432842 1001_ $$aPastore, Vito Paolo,$$eauthor. 001432842 24510 $$aEstimating functional connectivity and topology in large-scale neuronal assemblies :$$bstatistical and computational methods /$$cVito Paolo Pastore. 001432842 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021] 001432842 300__ $$a1 online resource (XV, 87 pages 43 illustrations, 39 illustrations in color.) 001432842 336__ $$atext$$btxt$$2rdacontent 001432842 337__ $$acomputer$$bc$$2rdamedia 001432842 338__ $$aonline resource$$bcr$$2rdacarrier 001432842 4901_ $$aSpringer theses : recognizing outstanding Ph. D. research,$$x2190-5053 001432842 500__ $$a"Doctoral thesis accepted by the University of Genova, Italy." 001432842 504__ $$aIncludes bibliographical references. 001432842 5050_ $$aIntroduction -- Materials and Methods -- Results -- Conclusion. 001432842 506__ $$aAccess limited to authorized users. 001432842 520__ $$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. 001432842 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 29, 2021). 001432842 650_0 $$aNeural networks (Neurobiology)$$xMathematical models. 001432842 650_0 $$aBiomedical engineering. 001432842 650_0 $$aElectrophysiology. 001432842 650_0 $$aNeural networks (Computer science) 001432842 650_0 $$aGraph theory. 001432842 650_6 $$aRéseaux neuronaux (Neurobiologie)$$xModèles mathématiques. 001432842 650_6 $$aGénie biomédical. 001432842 650_6 $$aÉlectrophysiologie. 001432842 650_6 $$aRéseaux neuronaux (Informatique) 001432842 655_0 $$aElectronic books. 001432842 77608 $$iPrint version:$$z9783030590413 001432842 830_0 $$aSpringer theses,$$x2190-5053 001432842 852__ $$bebk 001432842 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-59042-0$$zOnline Access$$91397441.1 001432842 909CO $$ooai:library.usi.edu:1432842$$pGLOBAL_SET 001432842 980__ $$aBIB 001432842 980__ $$aEBOOK 001432842 982__ $$aEbook 001432842 983__ $$aOnline 001432842 994__ $$a92$$bISE