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Neural Connectomics Challenge Softcover reprint of the original 1st ed. 2017 [Pehme köide]

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  • Formaat: Paperback / softback, 117 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 28 Illustrations, black and white; X, 117 p. 28 illus., 1 Paperback / softback
  • Sari: The Springer Series on Challenges in Machine Learning
  • Ilmumisaeg: 08-May-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319850547
  • ISBN-13: 9783319850542
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Neural Connectomics Challenge Softcover reprint of the original 1st ed. 2017Suurem pilt
  • Formaat: Paperback / softback, 117 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 28 Illustrations, black and white; X, 117 p. 28 illus., 1 Paperback / softback
  • Sari: The Springer Series on Challenges in Machine Learning
  • Ilmumisaeg: 08-May-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319850547
  • ISBN-13: 9783319850542
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319850542.html
This 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.
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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.


First 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.