Computational Neuroscience: A Comprehensive Approach, 1st Edition (Hardback) book cover

Computational Neuroscience

A Comprehensive Approach, 1st Edition

Edited by Jianfeng Feng, Jianfeng Feng

Chapman and Hall/CRC

656 pages | 171 B/W Illus.

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Hardback: 9781584883623
pub: 2003-10-20
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Description

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.

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

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

About the Series

Chapman & Hall/CRC Mathematical and Computational Biology

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Subject Categories

BISAC Subject Codes/Headings:
MAT003000
MATHEMATICS / Applied
MED090000
MEDICAL / Biostatistics
SCI089000
SCIENCE / Life Sciences / Neuroscience