Mathematical and theoretical neuroscience : cell, network and data analysis /: cell, network and data analysis. (2018)
- Record Type:
- Book
- Title:
- Mathematical and theoretical neuroscience : cell, network and data analysis /: cell, network and data analysis. (2018)
- Main Title:
- Mathematical and theoretical neuroscience : cell, network and data analysis
- Further Information:
- Note: Giovanni Naldi, Thierry Nieus, editors.
- Editors:
- Naldi, Giovanni
Nieus, Thierry - 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 Transition3.3 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 Function5 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 OutgrowthIntro; 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 Transition3.3 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 Function5 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 Response3.4 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 Prediction2.4 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) … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2018
- Extent:
- 1 online resource
- Subjects:
- 612.8
Mathematics
Neurosciences -- Mathematical models
Neurosciences
MEDICAL / Physiology
SCIENCE / Life Sciences / Human Anatomy & Physiology
Neurosciences
Neurosciences
Science -- Life Sciences -- General
Medical -- Biostatistics
Biology, life sciences
Probability & statistics
Biology_xData processing
Statistics
Mathematics -- Applied
Applied mathematics
Electronic books - Languages:
- English
- ISBNs:
- 9783319682976
3319682970 - Related ISBNs:
- 9783319682969
3319682962 - Notes:
- Note: Includes bibliographical references.
Note: Online resource; title from PDF title page (EBSCO, viewed March 28, 2018). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.358178
- Ingest File:
- 01_319.xml