Table of Contents

Chapter 1

Introduction to Neurons


1.1 Types of Neurons

1.1.1 Motoneurons

1.1.2 Vertebrate Peripheral Sensory Neurons

1.1.3 Neuroendocrine Cells

1.1.4 Interneurons

1.1.5 Discussion

1.2 Electrical Properties of Nerve Membrane

1.2.1 The Source of UM Electrical Parameters

1.2.2 Decremental Conduction on Dendrites: The Space Constant

1.2.3 Active Membrane: The Nerve Spike

1.2.4 Saltatory Conduction on Myelinated Axons

1.2.5 Discussion

1.3 Synapses: epsps and ipsps

1.3.1 Chemical Synapses

1.3.2 Electrical Synapses

1.3.3 epsps and ipsps

1.3.4 Quantal Release of Neurotransmitter

1.3.5 Discussion

1.4 Models for the Nerve Action Potential

1.4.1 The 1952 Hodgkin-Huxley Model for Action Potential Generation

1.4.2 Properties of the Hodgkin-Huxley Model

1.4.3 Extended Hodgkin-Huxley Models

1.4.4 Discussion

1.5 Chapter Summary Problems

Chapter 2

Selected Examples of Sensory Receptors and Small Receptor Arrays


2.1 The Generalized Receptor

2.1.1 Dynamic Response

2.1.2 Receptor Nonlinearity

2.1.3 Receptor Sensitivity

2.1.4 A Model for Optimum Firing Threshold

2.1.5 Simulation of a Model Receptor with a Continuously Variable Firing Threshold

2.1.6 Discussion

2.2 Chemoreceptors

2.2.1 The Vertebrate Olfactory Chemoreceptor

2.2.2 Olfaction in Arthropods

2.2.3 Discussion

2.3 Mechanoreceptors

2.3.1 Insect Trichoid Hairs

2.3.2 Insect Campaniform Sensilla

2.3.3 Muscle Length Receptors

2.3.4 Muscle Force Receptors

2.3.5 Statocysts

2.3.6 Pacinian Corpuscles

2.3.7 Discussion

2.4 Magnetoreceptors

2.4.1 Behavioral Evidence for Magnetic Sensing

2.4.2 The Putative Magnetoreceptor Neurons of Tritonia

2.4.3 Models for Magnetoreceptors

2.4.4 Discussion

2.5 Electroreceptors

2.5.1 Ampullary Receptors

2.5.2 Weakly Electric Fish and Knollenorgans

2.5.3 Discussion

2.6 Gravity Sensors of the Cockroach, Arenivaga sp.

2.6.1 Hartman's Methods

2.6.2 Hartman's Results

2.6.3 CNS Unit Activity Induced in Arenivaga by Roll and Pitch

2.6.4 Willey's Methods

2.6.5 Willey's Results

2.6.6 A Tentative Model for PCP Unit Narrow Sensitivity

2.6.7 Discussion

2.7 The Dipteran Haltere

2.7.1 The Torsional Vibrating Mass Gyro

2.7.2 Discussion

2.8 The Simple "Eye" of Mytilus

2.8.1 Eye Morphology

2.8.2 Physiology of the Eye

2.8.3 Discussion

2.9 Chapter Summary Problems

Chapter 3

Electronic Models of Neurons: A Historical Perspective


3.1 Neccesary Attributes of Small- and Medium-Scale Neural Models

3.2 Electronic Neural Models (Neuromimes)

3.3 Discussion

Chapter 4

Simulation of the Behavior of Small Assemblies of Neurons


4.1 Simulation of Synaptic Loci

4.1.1 A Linear Model for psp Generation

4.1.2 A Model for epsp Production Based on Chemical Kinetics

4.1.3 A Model for a Facilitating Synapse

4.1.4 A Model for an Antifacilitating Synapse

4.1.5 Inhibitory Synapses

4.1.6 Discussion

4.2 Dendrites and Local Response Loci

4.2.1 The Core-Conductor Transmission Line

4.2.2 Discussion

4.3 Integral and Relaxation Pulse Frequency Modulation Models for the Spike Generator Locus (SGL)

4.3.1 IPFM

4.3.2 RPFM

4.3.3 Modeling Adaptation

4.3.4 Modeling Neural Fatigue

4.3.5 Discussion

4.4 Theoretical Models for Neural Signal Conditioning

4.4.1 The T-Neuron

4.4.2 A Theoretical Band-Pass Structure: The Band Detector

4.4.3 Discussion

4.5 Recurrent Inhibition and Spike Train Pattern Generation

4.5.1 The Basic RI System

4.5.2 Szentagothai's RI Circuit

4.5.3 A Simple Burst Generator

4.5.4 A Ring CPG Model with Negative Feedback

4.5.5 Discussion

4.6 Chapter Summary Problems

Chaper 5

Large Arrays of Interacting Receptors: The Compound Eye


5.1 Anatomy of the Arthropod Compound Eye Visual System

5.1.1 Retinula Cells and Rhabdoms

5.1.2 The Optic Lobes

5.1.3 The Optics of the Compound Eye

5.1.4 Discussion

5.2 Spatial Resolution of the Compound Eye

5.2.1 The Compound Eye as a Two-Dimensional, Spatial Sampling Array

5.2.2 Calculation of Intensity Contrast

5.2.3 "Anomalous Resolution"

5.2.4 A Model for Contrast Enhancement in the Insect Visual System

5.2.5 A Hypothetical Model for Spatial Resolution Improvement in the Compound Eye by Synthetic Aperture

5.2.6 Discussion

5.3 Lateral Inhibition in the Eye of Limulus

5.3.1 Evidence for Lateral Inhibition

5.3.2 Modeling Lateral Inhibition as a Spatial Filter for Objects

5.3.3 Discussion

5.4 Feature Extraction by the Compound Eye System

5.4.1 Feature Extraction by Optic Lobe Units of Romalea

5.4.2 Feature Extraction by Optic Lobe Units in Flies

5.4.3 Eye Movements and Visual Tracking in Flies

5.4.4 Feature Extraction by Optic Lobe Units of Crustaceans

5.4.5 Discussion

5.5 Chapter Summary Problems

Chapter 6

Large Arrays of Interacting Receptors: The Vertebrate Retina


6.1 Review of the Anatomy and Physiology of the Vertebrate Retina

6.2 Feature Extraction by the Frog's Retina

6.2.1 Early Work

6.2.2 Directionally Sensitive Neurons in the Frog's Brain

6.3 Feature Extraction by Other Vertebrate Retinas

6.3.1 The Pigeon Retina

6.3.2 The Rabbit Retina

6.4 Chapter Summary

Chapter 7

Theoretical Models of Information Processing and Feature Extraction in Visual Sensory Arrays


7.1 Models for Neural Spatial Filters and Feature Extraction in Retinas

7.1.1 The Logic-Based, Spatiotemporal Filter Approach of Zorkoczy

7.1.2 Analog Models for Motion Detection in Insects

7.1.3 Continuous, Layered Visual Feature Extraction Filters

7.1.4 Discussion

7.2 Models for Neural Matched Filters in Vision

7.2.1 The Continuous, One-Dimensional, Spatial Matched Filter

7.2.2 The Continuous, Two-Dimensional Spatial Matched Filter

7.2.3 Discussion

7.3 Models for Parallel Processing: Artificial Neural Networks

7.3.1 Rosenblatt's Perceptron

7.3.2 Widrow's ADALINE and MADALINE

7.3.3 Fukushima's Neocognitron

7.3.4 Discussion

7.4 Chapter Summary Problems

Chapter 8

Characterization of Neuro-Sensory Systems

Review of Characterization and Identification Means for Linear Systems

8.1 Parsimonious Models for Neural Connectivity Based on Time Series Analysis of Spike Sequences

8.1.1 The JPST Diagram

8.1.2 Discussion

8.2 Triggered Correlation Applied to the Auditory System

8.2.1 Development for an Expression for the Conditional Expectation, x+(x)

8.2.2 Optimum Conditions for the Application of the TC Algorithm

8.2.3 Electronic Model Studies of TC

8.2.4 Neurophysiological Studies of Auditory Systems Using TC

8.2.5 Summary

8.3 The White Noise Method of Characterizing Nonlinear Systems

8.3.1 The Lee-Schetzen Approach to White Noise Analysis

8.3.2 Practical Aspects of Implementing the Lee-Schetzen White Noise Analysis

8.3.3 Applications of the White Noise Method to Neurobiological Systems

8.3.4 Discussion

8.4 Chapter Summary

Chapter 9

Software for Simulation of Neural Systems


9.1 XNBC v8

9.2 Neural Network Simulation Language, or NSL

9.3 Neuron


9.5 Other Neural Simulation Programs

9.5.1 EONS

9.5.2 SNNAP

9.5.3 SONN

9.6 Neural Modeling with General, Nonlinear System Simulation Software

9.6.1 Simnon

9.6.2 Simulink

9.7 Conclusion

Bibliography and References Appendix 1 Appendix 2 Appendix 3

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