Feng Computational Neuroscience

A Comprehensive Approach
Erscheinungsjahr 2003
ISBN: 978-1-135-44046-6
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 656 Seiten

Reihe: Chapman & Hall/CRC Mathematical and Computational Biology

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.

Zielgruppe

Mathematicians, physicists, engineers, electrophysicists, and systems neuroscientists

Autoren/Hrsg.

Feng, Jianfeng

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Vorklinische Medizin: Grundlagenfächer Physiologie
• Naturwissenschaften Biowissenschaften Tierkunde / Zoologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Neurologie, Klinische Neurowissenschaft
• Naturwissenschaften Biowissenschaften Biowissenschaften Neurobiologie, Verhaltensbiologie

Inhaltsverzeichnis

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