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BL
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| Record Number
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864086
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| Title & Author
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Mathematical and theoretical neuroscience : : cell, network and data analysis /\ Giovanni Naldi, Thierry Nieus, editors.
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| Publication Statement
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Cham, Switzerland :: Springer,, 2018.
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| Series Statement
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Springer INdAM series ;; volume 24
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| Page. NO
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1 online resource
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| ISBN
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3319682970
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: 9783319682976
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3319682962
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9783319682969
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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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
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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)
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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
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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
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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
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| Subject
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Neurosciences-- Mathematical models.
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| Subject
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Neurosciences.
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Neurosciences.
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Applied mathematics.
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Biology, life sciences.
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MEDICAL-- Physiology.
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Neurosciences.
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Probability statistics.
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SCIENCE-- Life Sciences-- Human Anatomy Physiology.
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| Dewey Classification
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612.8
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| LC Classification
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QP357.5
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| NLM classification
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WL 100
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| Added Entry
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Naldi, Giovanni
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Nieus, Thierry
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