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Introduction to Dynamic Modeling of Neuro-Sensory Systems




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ISBN 9780849308147
Published November 27, 2000 by CRC Press
488 Pages

 
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Book Description

Although neural modeling has a long history, most of the texts available on the subject are quite limited in scope, dealing primarily with the simulation of large-scale biological neural networks applicable to describing brain function. Introduction to Dynamic Modeling of Neuro-Sensory Systems presents the mathematical tools and methods that can describe and predict the dynamic behavior of single neurons, small assemblies of neurons devoted to a single tasks, as well as larger sensory arrays and their underlying neuropile.

Focusing on small and medium-sized biological neural networks, the author pays particular attention to visual feature extraction, especially the compound eye visual system and the vertebrate retina. For computational efficiency, the treatment avoids molecular details of neuron function and uses the locus approach for medium-scale modeling of arrays. Rather than requiring readers to learn a dedicated simulation program, the author uses the general, nonlinear ordinary differential equation solver Simnonä for all examples and exercises.

There is both art and science in setting up a computational model that can be validated from existing neurophysiological data. With clear prose, more than 200 figures and photographs, and unique focus, Introduction to Dynamic Modeling of Neuro-Sensory Systems develops the science, nurtures the art, and builds the foundation for more advanced work in neuroscience and the rapidly emerging field of neuroengineering.

Table of Contents

INTRODUCTION TO NEURONS
Introduction
Types of Neurons
Electrical Properties of Nerve Membrane
Synapses: epsps and ipsps
Models for the Nerve Action Potential
Chapter Summary
Problems
SELECTED EXAMPLES OF SENSORY RECEPTORS AND SMALL RECEPTOR ARRAYS
Introduction
The Generalized Receptor
Chemoreceptors
Mechanoreceptors
Magnetoreceptors
Electroreceptors
Gravity Sensors of the Cockroach: Arenivaga sp.
The Dipteran Haltere
The Simple "Eye" of Mytilus
Chapter Summary
Problems
ELECTRONIC MODELS OF NEURONS: A HISTORICAL PERSPECTIVE
Introduction
Necessary Attributes of Small- and Medium-Scale Neural Models
Electronic Neural Models (Neuromimes)
Discussion
SIMULATION OF THE BEHAVIOR OF SMALL ASSEMBLIES OF NEURONS
Introduction Simulation of Synaptic Loci
Dendrites and Local Response Loci
Integral and Relaxation Pulse Frequency Modulation Models for the Spike Generator Locus (SGL)
Theoretical Models for Neural Signal Conditioning
Recurrent Inhibition and Spike Train Pattern Generation
Chapter Summary
Problems
LARGE ARRAYS OF INTERACTING RECEPTORS: THE COMPOUND EYE
Introduction
Anatomy of the Arthropod Compound Eye Visual System
Spatial Resolution of the Compound Eye
Lateral Inhibition in the Eye of Limulus
Feature Extraction by the Compound Eye System
Chapter Summary
Problems
LARGE ARRAYS OF INTERACTING RECEPTORS: THE VERTEBRATE RETINA
Introduction
Review of the Anatomy and Physiology of the Vertebrate Retina
Feature Extraction by the Frog's Retina
Feature Extraction by Other Vertebrate Retinas
Chapter Summary
THEORETICAL MODELS OF INFORMATION PROCESSING AND FEATURE EXTRACTION IN VISUAL SENSORY ARRAYS
Introduction
Models for Neural Spatial Filters and Feature Extraction in Retinas
Models for Neural Matched Filters in Vision
Models for Parallel Processing: Artificial Neural Networks
Chapter Summary
Problems
CHARACTERIZATION OF NEURO-SENSORY SYSTEMS
Review of Characterization and Identification Means for Linear Systems
Parsimonious Models for Neural Connectivity Based on Time Series Analysis of Spike Sequences
Triggered Correlation Applied to the Auditory System
The White Noise Method of Characterizing Nonlinear Systems
Chapter Summary
SOFTWARE FOR SIMULATION OF NEURAL SYSTEMS
Introduction
XNBC v8
Neural Network Simulation Language, or NSL
Neuron
GENESIS
Other Neural Simulation Programs
Neural Modeling with General, Nonlinear System Simulation Software
Conclusion
BIBLIOGRAPHY AND REFERENCES
APPENDICES
INDEX

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