Computational Modeling Methods for Neuroscientists 1st Edition by Erik De Schutter ISBN 0262013274 9780262013277

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ISBN 10: 0262013274
ISBN 13: 9780262013277
Author: Erik De Schutter

This book offers an introduction to current methods in computational modeling in neuroscience. The book describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. It is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but it will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists. The chapters offer comprehensive coverage with little overlap and extensive cross-references, moving from basic building blocks to more complex applications.

Computational Modeling Methods for Neuroscientists 1st Table of contents:

• Neurobiological Foundations

  • Overview of Neurons and Synapses
  • Cellular and Network Models
  • Basic Neurophysiology: Action Potentials, Membrane Potentials, etc.
  • Neural Coding and Signal Transmission

• Mathematical Foundations for Modeling

  • Linear Algebra for Neuroscientists
  • Differential Equations in Modeling
  • Numerical Methods and Simulations
  • Stochastic Processes and Noise in Neural Systems

• Single Neuron Models

  • The Hodgkin-Huxley Model
  • The Leaky Integrate-and-Fire Model
  • Other Simplified Models of Neuron Dynamics
  • Firing Rate Models

• Synaptic and Cellular Interactions

  • Synaptic Transmission Models
  • Plasticity Models: Hebbian and Non-Hebbian Learning
  • Calcium Dynamics and Long-Term Potentiation (LTP)
  • Modeling Synaptic Interactions

• Network Models and Connectivity

  • Introduction to Neural Networks
  • Spiking Neural Networks
  • Recurrent Neural Networks and their Applications
  • Large-Scale Brain Network Models

• Modeling Sensory and Motor Systems

  • Sensory Processing Models: Vision, Auditory, etc.
  • Motor Control and Movement Models
  • Computational Models of Perception and Action

• Cognitive and Higher-Level Brain Function Models

  • Memory and Learning Models
  • Attention and Decision-Making
  • Models of Cognitive Neuroscience
  • Computational Models of Emotion and Motivation

• Neuroinformatics and Data-Driven Models

  • Handling Large-Scale Neural Data
  • Machine Learning in Computational Neuroscience
  • Model Validation and Fitting to Experimental Data
  • Data-Driven Brain Simulation

• Advanced Computational Techniques

  • Computational Models in Brain Imaging
  • The Role of Artificial Intelligence and Deep Learning in Neuroscience
  • Parallel Computing and High-Performance Simulation
  • Simulation Software and Tools for Neuroscientists

• Applications of Computational Neuroscience

  • Modeling Disease: Epilepsy, Parkinson’s, Alzheimer’s
  • Neuroprosthetics and Brain-Computer Interfaces
  • Computational Approaches in Cognitive Robotics
  • The Role of Computational Modeling in Translational Neuroscience

• Challenges and Future Directions

  • Open Questions in Computational Neuroscience
  • Ethical Considerations in Brain Modeling
  • The Future of Computational Neuroscience and AI Integration

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