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Mathematical and theoretical neuroscience [electronic resource] : cell, network and data analysis

Mathematical and theoretical neuroscience [electronic resource] : cell, network and data analysis

Material type
E-Book(소장)
Title Statement
Mathematical and theoretical neuroscience [electronic resource] : cell, network and data analysis / Giovanni Naldi, Thierry Nieus, editors.
Publication, Distribution, etc
Cham : Springer, c2017.
Physical Medium
1 online resource (ix, 253 p.).
Series Statement
Springer INdAM series,2281-518X ; 24
ISBN
9783319682969 9783319682976 (eBook)
요약
This volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience. The focus is on mathematical modeling, analytical and numerical topics, and statistical analysis in neuroscience with applications. The following subjects are considered: mathematical modelling in Neuroscience, analytical  and numerical topics;  statistical analysis in Neuroscience; Neural Networks; Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience.
General Note
Title from e-Book title page.
Content Notes
1 Simulating cortical Local Field Potentials and Thalamus dynamic regimes with integrate-and-fire neurons -- 2 Computational modeling as a means to defining neuronal spike pattern behaviors -- 3 Chemotactic guidance of growth cones: a hybrid computational model -- 4 Mathematical Modeling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions -- 5 Bifurcation analysis of a sparse neural network with cubic topology -- 6 Simultaneous jumps in interacting particle systems: from neuronal networks to a general framework -- 7 Neural fields: Localised states with piece-wise constant interactions -- 8 Mathematical models of visual perception based on cortical architectures -- 9 Mathematical models of visual perception for the analysis of Geometrical optical illusions -- 10 Exergaming for autonomous rehabilitation -- 11 E-infrastructures for neuroscientists: the GAAIN and neuGRID examples -- 12 Nonlinear Time series Analysis -- 13 Measures of spike train synchrony and Directionality -- 14 Space-by-time tensor decomposition of single-trial analysis of neural signals -- 15 Inverse Modeling for MEG/EEG data.
Bibliography, Etc. Note
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.
Subject Added Entry-Topical Term
Neurosciences --Mathematical models. Neurosciences.
Short cut
URL
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020 ▼a 9783319682969
020 ▼a 9783319682976 (eBook)
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245 0 0 ▼a Mathematical and theoretical neuroscience ▼h [electronic resource] : ▼b cell, network and data analysis / ▼c Giovanni Naldi, Thierry Nieus, editors.
260 ▼a Cham : ▼b Springer, ▼c c2017.
300 ▼a 1 online resource (ix, 253 p.).
490 1 ▼a Springer INdAM series, ▼x 2281-518X ; ▼v 24
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
505 0 ▼a 1 Simulating cortical Local Field Potentials and Thalamus dynamic regimes with integrate-and-fire neurons -- 2 Computational modeling as a means to defining neuronal spike pattern behaviors -- 3 Chemotactic guidance of growth cones: a hybrid computational model -- 4 Mathematical Modeling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions -- 5 Bifurcation analysis of a sparse neural network with cubic topology -- 6 Simultaneous jumps in interacting particle systems: from neuronal networks to a general framework -- 7 Neural fields: Localised states with piece-wise constant interactions -- 8 Mathematical models of visual perception based on cortical architectures -- 9 Mathematical models of visual perception for the analysis of Geometrical optical illusions -- 10 Exergaming for autonomous rehabilitation -- 11 E-infrastructures for neuroscientists: the GAAIN and neuGRID examples -- 12 Nonlinear Time series Analysis -- 13 Measures of spike train synchrony and Directionality -- 14 Space-by-time tensor decomposition of single-trial analysis of neural signals -- 15 Inverse Modeling for MEG/EEG data.
520 ▼a This volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience. The focus is on mathematical modeling, analytical and numerical topics, and statistical analysis in neuroscience with applications. The following subjects are considered: mathematical modelling in Neuroscience, analytical  and numerical topics;  statistical analysis in Neuroscience; Neural Networks; Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Neurosciences ▼x Mathematical models.
650 0 ▼a Neurosciences.
700 1 ▼a Naldi, Giovanni.
700 1 ▼a Nieus, Thierry.
830 0 ▼a Springer INdAM series ; ▼v 24.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-68297-6
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. E14014505 Availability Loan can not(reference room) Due Date Make a Reservation Service M

Contents information

Author Introduction

Giovanni Naldi(엮은이)

Thierry Nieus(엮은이)

Information Provided By: : Aladin

Table of Contents

Intro -- Preface -- Contents -- About the Authors -- From Single Neuron Activity to Network Information Processing: Simulating Cortical Local Field Potentials and Thalamus Dynamic Regimes with Integrate-and-Fire Neurons -- 1 The Map and the Territory -- 2 Simulating Local Field Potential with Integrate and Fire Neurons -- 2.1 Problems and Solutions -- 2.2 Combining Integrate-and-Fire Neurons and Morphological Models -- 2.3 Combining IFN Networks and Morphological Simulations -- 3 Integrate and Fire Neurons Model of the Thalamus -- 3.1 Thalamic Neurons Modeling -- 3.2 Integrate-and-Fire Model of the Thalamus Reproduces Sleep/Wake Information Processing Transition -- 3.3 Perspectives -- References -- Computational Modeling as a Means to Defining Neuronal Spike Pattern Behaviors -- 1 Introduction -- 2 Computational Model of a Neuron -- 2.1 Neuro-computational Properties -- 2.2 Biophysically Meaningful Models -- 2.3 Integrate and Fire (IF) Models -- 2.4 Izhikevich Model -- 3 Spike Pattern Behaviors -- 4 Evolutionary Algorithm as a Tool for Modeling Neuronal Dynamics -- 4.1 Model Optimization Using the EA -- 4.2 Feature-Based Fitness Function -- 4.3 Fitness Landscape with a Feature Based Function -- 5 Modeling Spike Pattern Behaviors -- 5.1 Optimization Objectives with a Behavior -- 5.2 Parameter Space Exploration -- 6 Summary -- References -- Chemotactic Guidance of Growth Cones: A Hybrid Computational Model -- 1 Introduction -- 2 Methods -- 2.1 Evolution of Intracellular Chemical Fields Within the GC Domain -- 2.2 Computational Model of Axonal Outgrowth Guided by Chemotaxis -- 2.3 Quantitative Evaluation of Growth Cone Model Performance -- 3 Results -- 3.1 Diffusion-Driven Instability -- 3.2 In Silico Paths of Outgrowing Axons -- 3.3 Quantitative Assessment of the Axonal Chemoattractive Response -- 3.4 Quantitative Assessment of Axonal Outgrowth in Control Conditions -- 3.5 Qualitative Predictions of Axonal Counterintuitive Behaviours -- 4 Discussion -- References -- Mathematical Modelling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions -- 1 Introduction -- 2 Methods -- 2.1 Single Neuron Modeling -- 2.2 Cerebellar Granular Layer Information Processing -- 2.3 Model Based Methods for Hemodynamic Response -- 2.3.1 Balloon Model Based Prediction -- 2.3.2 Modified Windkessel Model Based Prediction -- 2.4 Evoked Local Field Potentials and Neural Mass Model -- 2.4.1 Cerebellum Granular Layer Neural Mass Model with Mossy Fibers Input Patterns -- 2.4.2 Reconstruction of Local Field Potential from Spiking Models -- 3 Spiking Neural Network Based on Cerebellum for Kinematics -- 4 Results -- 4.1 Estimation of MI at MF-GrC Relay -- 4.2 Variations in BOLD Response Measured Using Balloon Model and Modified Windkessel Model (MFWM) -- 4.3 Simulating Extracellular Potentials Recordings in Neural Mass Model (NMM) and Spiking Neural Network (SNN) -- 4.4 Optimized Kinematic Control Using SN.

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