HOME > Detail View

Detail View

Neural connectomics challenge [electronic resource]

Neural connectomics challenge [electronic resource]

Material type
E-Book(소장)
Personal Author
Title Statement
Neural connectomics challenge [electronic resource] / Demian Battaglia ... [et al.], editors.
Publication, Distribution, etc
Cham : Springer, c2017.
Physical Medium
1 online resource (x, 117 p.) : ill. (some col.).
Series Statement
The springer series on challenges in machine learning,2520-131X
ISBN
9783319530697 9783319530703 (eBook)
요약
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. < 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.
General Note
Title from e-Book title page.
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.
Subject Added Entry-Topical Term
Computational neuroscience. Neural networks (Neurobiology).
Short cut
URL
000 00000cam u2200205 a 4500
001 000045988535
005 20190703120050
006 m d
007 cr
008 190703s2017 sz a ob 000 0 eng d
020 ▼a 9783319530697
020 ▼a 9783319530703 (eBook)
040 ▼a 211009 ▼c 211009 ▼d 211009
050 4 ▼a Q334-342
082 0 4 ▼a 612.8 ▼2 23
084 ▼a 612.8 ▼2 DDCK
090 ▼a 612.8
245 0 0 ▼a Neural connectomics challenge ▼h [electronic resource] / ▼c Demian Battaglia ... [et al.], editors.
260 ▼a Cham : ▼b Springer, ▼c c2017.
300 ▼a 1 online resource (x, 117 p.) : ▼b ill. (some col.).
490 1 ▼a The springer series on challenges in machine learning, ▼x 2520-131X
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
505 0 ▼a 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.
520 ▼a 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. < 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Computational neuroscience.
650 0 ▼a Neural networks (Neurobiology).
700 1 ▼a Battaglia, Demian.
830 0 ▼a Springer series on challenges in machine learning.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-53070-3
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 612.8 Accession No. E14014287 Availability Loan can not(reference room) Due Date Make a Reservation Service M

Contents information

Author Introduction

Isabelle Guyon(엮은이)

Vincent Lemaire(엮은이)

Demian Battaglia(엮은이)

Information Provided By: : Aladin

Table of Contents

Intro -- Foreword -- Preface -- Contents -- First Connectomics Challenge: From Imaging to Connectivity -- 1 Introduction -- 2 Challenge Design -- 3 Results -- 3.1 Challenge Duration -- 3.2 Overfitting -- 3.3 PR Curves -- 3.4 Edge Orientation -- 3.5 Subnetworks -- 4 Methods -- 5 Conclusions -- References -- Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging -- 1 Introduction -- 2 Signal Processing -- 3 Connectome Inference from Partial Correlation Statistics -- 4 Experiments -- 5 Conclusions -- A.1 Signal Processing -- A.2 Weighted Average of Partial Correlation Statistics -- A.3 Prediction of Edge Orientation -- A.4 Experiments -- References -- Supervised Neural Network Structure Recovery -- 1 Introduction -- 2 Model -- 2.1 Spike Inference -- 2.2 Connectivity Indicators -- 2.3 Network Deconvolution -- 2.4 Modeling Approach -- 3 Evaluation -- 4 Conclusions and Future Work -- References -- Signal Correlation Prediction Using Convolutional Neural Networks -- 1 Introduction -- 2 Dataset and Evaluation -- 3 CNN Model -- 3.1 The First Solution: Basic Approach -- 3.2 Background on Convolutional Neural Networks -- 3.3 Introduction to CNN Filter and CNN Model -- 3.4 CNN Filter Key Time Series Processing Methods -- 3.5 CNN Filter Structure -- 3.6 On the CNN Model Implementation and Development -- 4 Results -- 5 Conclusions -- References -- Reconstruction of Excitatory Neuronal Connectivity via Metric Score Pooling and Regularization -- 1 Introduction -- 2 Methods -- 2.1 Preprocessing of Calcium Imaging -- 2.2 Csiszár''s Transfer Entropy -- 2.3 Correlation Metrics -- 2.4 Pooling of Different Metric Scores -- 2.5 Regularization on the Recovered Network -- 2.6 Evaluation of the Reconstruction Performance -- 3 Results -- 3.1 CTE -- 3.2 Pooling Metrics Scores -- 3.3 Network Regularization -- 3.4 Challenge Results -- 4 Discussion -- References -- Neural Connectivity Reconstruction from Calcium Imaging Signal Using Random Forest with Topological Features -- 1 Introduction -- 2 Methods -- 2.1 Efficient Features Extraction -- 2.2 Random Forest -- 2.3 Random Forest with Topological Features -- 2.4 Random Forest Training with Constant Representation Changes -- 3 Evaluation -- 4 Conclusions -- References -- Efficient Combination of Pairwise Feature Networks -- 1 Introduction -- 2 Typical Methods -- 2.1 Correlation with Discretization -- 2.2 Generalized Transfer Entropy -- 3 Our Proposal: Unsupervised Ensemble of CLRed Pairwise Features -- 3.1 Feature 1: Symmetrized GTE -- 3.2 Feature 2: Correlation of the Extrema of the Signals -- 3.3 Feature 3: Mean Squared Error of Difference Signal -- 3.4 Feature 4: Range of Difference Signal -- 4 Experiments -- 5 Conclusion -- References -- Predicting Spiking Activities in DLS Neurons with Linear-Nonlinear-Poisson Model -- 1 Introduction -- 1.1 Dorsolateral Striatum Single Body Part Neurons -- 1.2 The Linear-Nonlinear-Poisson Model -- 2 Methods -- 2.1 Data Collection and Preprocessing -- 2.2 Experimental Design -- 3 Results -- 3.1 Predicting Neural Activity with Features from All Modalities -- 3.2 Predicting Neural Activity with Features from Individual Modalities -- 3.3 Comparing Performances of Single Modality Versus All Modalities Combined -- 4 Discussion -- References -- SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Optimization Task -- 3.2 Corresponding Pixels -- 4 Neuronal Segmentation -- 5 Experiments -- 5.1 ssTEM Imaging and Neuronal Reconstruction -- 5.2 Natural Videos -- 6 Conclusion -- References -- Appendix A Supplemental Information -- .

New Arrivals Books in Related Fields

Mader, Sylvia S (2020)
Fox, Stuart Ira (2020)
김덕훈 (2020)
고석신 (2020)
Müller-Grünow, Robert (2020)
Babetto, Elisabetta (2020)