000 04760nam a22006255i 4500
001 978-3-319-53070-3
003 DE-He213
005 20210118135707.0
007 cr nn 008mamaa
008 170504s2017 gw | s |||| 0|eng d
020 _a9783319530703
_9978-3-319-53070-3
024 7 _a10.1007/978-3-319-53070-3
_2doi
050 4 _aQ334-342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aNeural Connectomics Challenge
_h[electronic resource] /
_cedited by Demian Battaglia, Isabelle Guyon, Vincent Lemaire, Javier Orlandi, Bisakha Ray, Jordi Soriano.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aX, 117 p. 28 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Springer Series on Challenges in Machine Learning,
_x2520-131X
505 0 _aFirst Connectomics Challenge: From Imaging to Connectivity -- Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging -- Supervised Neural Network Structure Recovery -- Signal Correlation Prediction Using Convolutional Neural Networks -- Reconstruction of Excitatory Neuronal Connectivity via Metric Score Pooling and Regularization -- Neural Connectivity Reconstruction from Calcium Imaging Signal using Random Forest with Topological Features -- Efficient Combination of Pairwise Feature Networks -- Predicting Spiking Activities in DLS Neurons with Linear-Nonlinear-Poisson Model -- SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data -- Supplemental Information.
520 _aThis book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience. While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a problem of interest in a wide variety of domains, including econometrics, epidemiology, and climatology, to cite only a few. < The book is designed for the mathematics, physics and computer science communities that carry out research in neuroscience problems. The content is also suitable for the machine learning community because it exemplifies how to approach the same problem from different perspectives.
650 0 _aArtificial intelligence.
650 0 _aOptical data processing.
650 1 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aImage Processing and Computer Vision.
_0http://scigraph.springernature.com/things/product-market-codes/I22021
700 1 _aBattaglia, Demian.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGuyon, Isabelle.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLemaire, Vincent.
_eeditor.
_0(orcid)0000-0002-6030-2356
_1https://orcid.org/0000-0002-6030-2356
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aOrlandi, Javier.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aRay, Bisakha.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSoriano, Jordi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319530697
776 0 8 _iPrinted edition:
_z9783319530710
776 0 8 _iPrinted edition:
_z9783319850542
830 0 _aThe Springer Series on Challenges in Machine Learning,
_x2520-131X
856 4 0 _uhttps://doi.org/10.1007/978-3-319-53070-3
912 _aZDB-2-SCS
999 _c449411
_d449411
942 _cEB
506 _aAvailable to subscribing member institutions only. Доступно лише організаціям членам підписки.
506 _fOnline access from local network of NaUOA.
506 _fOnline access with authorization at https://link.springer.com/
506 _fОнлайн-доступ з локальної мережі НаУОА.
506 _fОнлайн доступ з авторизацією на https://link.springer.com/