The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System

'I Neurobiological Tutorials with GENESIS.- 1 Introduction.- 1.1 Computational Neuroscience.- 1.2 Using This Book.- 2 Compartmental Modeling.- 2.1 Modeling Neurons.- 2.1.1 Detailed Compartmental Models.- 2.1.2 Equivalent Cylinder Models.- 2.1.3 Single and Few Compartment Models.- 2.2 Equivalent Circuit of a Single Compartment.- 2.3 Axonal Connections, Synapses and Networks.- 2.4 Simulation Accuracy.- 2.4.1 Choice of Numerical Integration Technique.- 2.4.2 Integration Time Step.- 2.4.3 Accuracy of GENESIS.- 3 Neural Modeling with GENESIS.- 3.1 What Is GENESIS?.- 3.1.1 Why Use a General Simulator?.- 3.1.2 GENESIS Design Features.- 3.1.3 GENESIS Development.- 3.2 Introduction to the Tutorials.- 3.3 Introduction to the GENESIS Graphical Interface.- 3.3.1 Starting the Simulation.- 3.3.2 The Control Panel.- 3.3.3 Using Help Menus.- 3.3.4 Displaying the Simulation Results.- 4 The Hodgkin-Huxley Model.- 4.1 Introduction.- 4.2 Historical Background.- 4.3 The Mathematical Model.- 4.3.1 Electrical Equivalent Circuit.- 4.3.2 HH Conventions.- 4.3.3 The Ionic Current.- 4.4 Voltage Clamp Experiments.- 4.4.1 Characterizing the K Conductance.- 4.5 GENESIS: Voltage Clamp Experiments.- 4.6 Parameterizing the Rate Constants.- 4.7 Inactivation of the Na Conductance.- 4.8 Current Injection Experiments.- 4.9 Exercises.- 5 Cable and Compartmental Models of Dendritic Trees.- 5.1 Introduction.- 5.2 Background.- 5.2.1 Dendritic Trees: Anatomy, Physiology and Synaptology.- 5.2.2 Summary.- 5.3 The One-Dimensional Cable Equation.- 5.3.1 Basic Concepts and Assumptions.- 5.3.2 The Cable Equation.- 5.4 Solution of the Cable Equation for Several Cases.- 5.4.1 Steady-State Voltage Attenuation with Distance.- 5.4.2 Voltage Decay with Time.- 5.4.3 Functional Significance of ? and ?m.- 5.4.4 The Input Resistance Rin and "Trees Equivalent to a Cylinder".- 5.4.5 Summary of Main Results from the Cable Equation.- 5.5 Compartmental Modeling Approach.- 5.6 Compartmental Modeling Experiments.- 5.7 Main Insights for Passive Dendrites with Synapses.- 5.8 Biophysics of Excitable Dendrites.- 5.9 Computational Function of Dendrites.- 5.10 Exercises.- 6 Temporal Interactions Between Postsynaptic Potentials.- 6.1 Introduction.- 6.2 Electrical Model of a Patch of Membrane.- 6.2.1 Voltage Response of Passive Membrane to a Current Pulse.- 6.3 Response to Activation of Synaptic Channels.- 6.3.1 The Postsynaptic Current.- 6.3.2 The Postsynaptic Potential.- 6.3.3 Smooth Synaptic Conductance Change: The "Alpha Function".- 6.4 A Remark on Synaptic Excitation and Inhibition.- 6.5 GENESIS Experiments with PSPs.- 6.5.1 Temporal Summation of Postsynaptic Potentials.- 6.5.2 Nonlinear Summation of Postsynaptic Potentials.- 6.6 Concluding Remarks.- 6.7 Exercises and Projects.- 7 Ion Channels in Bursting Neurons.- 7.1 Introduction.- 7.2 General Properties of Molluscan Neurons.- 7.3 Ionic Conductances - The Dance of the Ions.- 7.3.1 Action Potential Related Conductances.- 7.3.2 Control of Bursting Properties.- 7.4 A Model Molluscan Neuron.- 7.4.1 Adrift in Parameter Space.- 7.4.2 Implementation of the Model.- 7.4.3 Modeling the Channels.- 7.5 The Molluscan Neuron Simulation.- 7.5.1 Using Neurokit.- 7.5.2 Understanding the Results.- 7.6 The Traub Model CA3 Pyramidal Cell.- 7.6.1 Experiments with the Traub Model.- 7.6.2 Firing Patterns.- 7.7 Exercises.- 8 Central Pattern Generators.- 8.1 Introduction.- 8.2 Two-Neuron Oscillators.- 8.2.1 Phase Equation Model of Coupled Oscillators.- 8.2.2 Simulation Parameters.- 8.2.3 Initial Conditions.- 8.2.4 Synaptic Coupling.- 8.2.5 Non-Phase Equation Models.- 8.3 Four-Neuron Oscillators.- 8.3.1 Chains of Coupled Oscillators.- 8.3.2 Simulation Parameters.- 8.3.3 Modeling Gaits.- 8.4 Summary.- 8.5 Exercises.- 9 Dynamics of Cerebral Cortical Networks.- 9.1 Introduction.- 9.2 Piriform Cortex.- 9.3 Structure of the Model.- 9.3.1 Cellular Complexity.- 9.3.2 Network Ci