Computational Neuroscience

Computational Neuroscience: A Comprehensive Approach

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Features

  • Balances the theoretical and experimental aspects of the field with chapters written by biologists as well as mathematicians
  • Covers all levels of modeling, from atomic-level modeling of single channels to modeling visual attention
  • Addresses cutting-edge topics such as calcium activity and learning rules
  • Includes a chapter on modeling motor control that helps close the gap between sensory input and motor output
  • Proposes a model for neural microcircuits that challenges traditional approaches to neural coding and suggests new ways of modeling cognitive processing
  • Summary

    How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding.

    Computational Neuroscience: A Comprehensive Approach provides a unified treatment of the mathematical theory of the nervous system and presents concrete examples demonstrating how computational techniques can illuminate difficult neuroscience problems. In chapters contributed by top researchers, the book introduces the basic mathematical concepts, then examines modeling at all levels, from single-channel and single neuron modeling to neuronal networks and system-level modeling. The emphasis is on models with close ties to experimental observations and data, and the authors review application of the models to systems such as olfactory bulbs, fly vision, and sensorymotor systems.

    Understanding the nature and limits of the strategies neural systems employ to process and transmit sensory information stands among the most exciting and difficult challenges faced by modern science. This book clearly shows how computational neuroscience has and will continue to help meet that challenge.

    Table of Contents

    A THEORETICAL OVERVIEW
    Introduction
    Deterministic Dynamical Systems
    Stochastic Dynamical Systems
    Information Theory
    Optimal Control
    ATOMISTIC SIMULATIONS OF ION CHANNELS
    Introduction
    Simulation Methods
    Selected Applications
    Outlook
    MODELING NEURONAL CALCIUM DYNAMICS
    Introduction
    Basic Principles
    Special Calcium Signaling for Neurons
    Conclusions
    STRUCTURE BASED MODELS OF NO DIFFUSION IN THE NERVOUS SYSTEM
    Introduction
    Methods
    Results
    Exploring Functional Roles with More Abstract Models
    Conclusions
    STOCHASTIC MODELING OF SINGLE ION CHANNELS
    Introduction
    Some Basic Probability
    Single Channel Models
    Transition Probabilities, Macroscopic Currents and Noise
    Macroscopic Currents and Noise
    Behaviour of Single Channels under Equilibrium Conditions
    Time Interval Omission
    Some Miscellaneous Topics
    THE BIOPHYSICAL BASIS OF FIRING VARIABILITY IN CORTICAL NEURONS
    Introduction
    Typical Input is Correlated and Irregular
    Synaptic Unreliability
    Postsynaptic Ion Channel Noise
    Integration of a Transient Input by Cortical Neurons
    Noisy Spike Generation Dynamics
    Dynamics of NMDA Receptors
    Class 1 and Class 2 Neurons Show Different Noise Sensitivities
    Cortical Cell Dynamical Classes
    Implications for Synchronous Firing
    Conclusions
    Generating Models of Single Neurons
    Introduction
    The Hypothalamo-Hypophysial System
    Statistical Methods to Investigate The Intrinsic Mechanisms Underlying Spike Patterning
    Summary and Conclusions
    GENERATING QUANTITATIVELY ACCURATE, BUT COMPUTATIONALLY CONCISE, MODELS OF SINGLE NEURONS
    Introduction
    The Hypothalamo-hypophysial System
    Statistical Methods to Investigate the Intrinsic Mechanisms Underlying Spike Patterning
    Summary and Conclusions
    BURSTING ACTIVITY IN WEAKLY ELECTRIC FISH
    Introduction
    Overview of the Electrosensory System
    Feature Extraction by Spike Bursts
    Factors Shaping Burst Firing In Vivo
    Conditional Action Potential Back Propagation Controls Burst Firing In Vitro
    Comparison with Other Bursting Neurons
    Conclusions
    LIKELIHOOD METHODS FOR NEURAL SPIKE TRAIN DATA ANALYSIS
    Introduction
    Theory
    Applications
    Conclusion
    Appendix
    BIOLOGICALLY-DETAILED NETWORK MODELING
    Introduction
    Cells
    Synapses
    Connections
    Inputs
    Implementation
    Validation
    Conclusions
    HEBBIAN LEARNING AND SPIKE-TIMING-DEPENDENT PLASTICITY
    Hebbian Models of Plasticity
    Spike-Timing Dependent Plasticity
    Role of Constraints in Hebbian Learning
    Competitive Hebbian Learning Through STDP
    Temporal Aspects of STDP
    STDP in a Network
    Conclusion
    CORRELATED NEURONAL ACTIVITY: HIGH-AND LOW-LEVEL VIEWS
    Introduction: the Timing Game
    Functional Roles for Spike Timing
    Correlations Arising from Common input
    Correlations Arising from Local Network Interactions
    When Are Neurons Sensitive to Correlated Input?
    A Simple, Quantitative Model
    Correlations and Neuronal Variability
    Conclusion
    Appendix
    A CASE STUDY OF POPULATION CODING: STIMULUS LOCALIZATION IN THE BARREL CORTEX
    Introduction
    Series Expansion Method
    The Whisker System
    Coding in the Whisker System
    Discussion
    Conclusions
    MODELING FLY MOTION VISION
    The Fly Motion Vision System: An Overview
    Mechanisms of Local Motion Detection: The Correlation Detector
    Spatial Processing of Local Motion Signals BY Lobula Plate Tangential Cells
    Conclusions
    MEAN-FIELD THEORY OF IRREGULARLY SPIKING NEURONAL POPULATIONS AND WORKING MEMORY IN RECURRENT CORTICAL NETWORKS
    Introduction
    Firing-Rate and Variability of a Spiking Neuron with Noisy input
    Self-Consistent Theory of Recurrent Cortical Circuits
    THE OPERATION OF MEMORY SYSTEMS IN THE BRAIN
    Introduction
    Functions of the Hippocampus in Long-Term Memory
    Short Term Memory Systems
    Invariant Visual Object Recognition
    Visual Stimulus-Reward Association, Emotion, and Motivation
    Effects of Mood on Memory and Visual Processing
    MODELING MOTOR CONTROL PARADIGMS
    Introduction: The Ecological Nature of Motor Control
    The Robotic Perspective
    The Biological Perspective
    The Role of Cerebellum in the Coordination of Multiple Joints
    Controlling Unstable Plants
    Motor Learning Paradigms
    COMPUTATIONAL MODELS FOR GENERIC CORTICAL MICROCIRCUITS
    Introduction
    A Conceptual Framework for Real-Time Neural Computation
    The Generic Neural Microcircuit Model
    Towards a Non-Turing theory for Real-Time Neural Computation
    A Generic Neural Microcircuit on the Computational Test Stand
    Temporal integration and Kernel Function of Neural Microcircuit Models
    Software for Evaluating the Computational Capabilities of Neural Microcircuit Models
    Discussion
    MODELING PRIMATE VISUAL ATTENTION
    Introduction
    Brain Areas
    Bottom-Up Control
    Top-Down Modulation of Early Vision
    Top-Down Deployment of Attention
    Attention and Scene Understanding
    Discussion

    Editorial Reviews

    "It is recommended for researchers and graduate students who want to enter the field or to acquire some knowledge on the current state of modeling for getting new research directions…the reader can use this book as a good and concise instrument for finding new perspectives for research."
    - Mathematical Reviews, 2005h

     
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