Wiki source code of Neuron

Version 219.1 by abonard on 2025/06/03 11:05
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adavison 1.1 1
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abonard 3.1 3 * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
jessicamitchell 2.1 4
abonard 198.1 5 * ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
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abonard 3.1 7 === **Beginner** ===
jessicamitchell 2.1 8
9 === [[A NEURON Programming Tutorial - part C>>http://web.mit.edu/neuron_v7.4/nrntuthtml/tutorial/tutC.html||rel=" noopener noreferrer" target="_blank"]] ===
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abonard 3.1 11 **Level**: beginner(%%) **Type**: user documentation
jessicamitchell 2.1 12
abonard 3.1 13 After this tutorial, students will be able to replicate neurons using templates and connect these neurons together.
abonard 190.1 14 === [[A NEURON Programming Tutorial - Part A>>http://web.mit.edu/neuron_v7.4/nrntuthtml/tutorial/tutA.html||rel=" noopener noreferrer" target="_blank"]] ===
jessicamitchell 2.1 15
abonard 190.1 16 **Level**: beginner(%%) **Type**: user documentation
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18 After this tutorial, students will be able to know how to create a single compartment neuron model with Hodgkin-Huxley conductances, how to run the simulator and how to display the simulation results
abonard 191.1 19 === [[A NEURON Programming Tutorial - Part B>>http://web.mit.edu/neuron_v7.4/nrntuthtml/tutorial/tutB.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 190.1 20
abonard 191.1 21 **Level**: beginner(%%) **Type**: user documentation
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23 After this tutorial, students will be able to work with more advanced topics of building multi-compartmental neurons and using different types of graphs to display the results
abonard 192.1 24 === [[A NEURON Programming Tutorial - Part D>>http://web.mit.edu/neuron_v7.4/nrntuthtml/tutorial/tutE.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 191.1 25
abonard 192.1 26 **Level**: beginner(%%) **Type**: user documentation
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28 After this tutorial, students will be able to add new membrane mechanisms to the simulator and incorporate them in our neurons.
abonard 193.1 29 === [[Construction and Use of Models: Part 1. Elementary tools>>https://neuron.yale.edu/neuron/static/docs/elementarytools/outline.htm||rel=" noopener noreferrer" target="_blank"]] ===
abonard 192.1 30
abonard 193.1 31 **Level**: beginner(%%) **Type**: user documentation
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33 A good beginner's tutorial to get an introduction to some of NEURON's basic GUI tools.
abonard 194.1 34 === [[A NEURON Programming Tutorial - Introduction>>https://web.mit.edu/neuron_v7.4/nrntuthtml/index.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 193.1 35
abonard 194.1 36 **Level**: beginner(%%) **Type**: user documentation
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38 This is a web based tutorial in the NEURON Simulation package. It will hopefully take you step by step, through the process of creating a complex simulation of a small network of neurons.
39 Starting by creating a single compartment neuron model with Hodgkin-Huxley conductances, how to run the simulator and how to display the simulation results, building multi-compartmental neurons, using different types of graphs to display the results, how to replicate neurons using templates, add new membrane mechanisms to the simulator and incorporate them into our neurons, increasing simulation speed and ways of getting data out of NEURON.
abonard 195.1 40 === [[Outline of "Construction and Use of Models: Part 1. Elementary tools">>https://neuron.yale.edu/neuron/static/docs/elementarytools/outline.htm||rel=" noopener noreferrer" target="_blank"]] ===
abonard 194.1 41
abonard 195.1 42 **Level**: beginner(%%) **Type**: interactive tutorial
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44 In this beginner tutorial you will learn how to make a simple model using hoc and how to use NEURON's graphical tools to create an interface for running simulations and to modify the model itself.
abonard 196.1 45 === [[The hoc programming language>>https://neuron.yale.edu/neuron/static/docs/programming/hoc_slides.pdf||rel=" noopener noreferrer" target="_blank"]] ===
abonard 195.1 46
abonard 196.1 47 **Level**: beginner(%%) **Type**: slide deck
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49 Slides from a presentation on hoc syntax. Clear and concise. Includes an example of program analysis (walkthrough of code for a model cell generated by the CellBuilder).
abonard 197.1 50 === [[A NEURON Programming Tutorial - Part E>>http://web.mit.edu/neuron_v7.4/nrntuthtml/tutorial/tutE.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 196.1 51
abonard 197.1 52 **Level**: beginner(%%) **Type**: user documentation
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54 After this tutorial, students will be able to save data from the simulations and methods for increasing simulation speed.
abonard 198.1 55 === **Advanced** ===
abonard 197.1 56
abonard 198.1 57 === [[Reaction-Diffusion – Radial Diffusion>>https://neuron.yale.edu/neuron/docs/radial-diffusion||rel=" noopener noreferrer" target="_blank"]] ===
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59 **Level**: advanced(%%) **Type**: -
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61 Using NEURON Radial diffusion can be implemented in rxd using multicompartment reactions. By creating a series of shells and borders with reactions between them dependent the diffusion coefficient.
abonard 199.1 62 === [[Reaction-Diffusion Example – Calcium Wave>>https://neuron.yale.edu/neuron/docs/reaction-diffusion-calcium-wave||rel=" noopener noreferrer" target="_blank"]] ===
abonard 198.1 63
abonard 199.1 64 **Level**: advanced(%%) **Type**: interactive tutorial
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66 The model presented in this tutorial generates Ca2+ waves and is a simplification of the model we used in Neymotin et al., 2015.
abonard 200.1 67 === [[Reaction-Diffusion – 3D/Hybrid Intracellular Tutorial>>https://neuron.yale.edu/neuron/docs/3dhybrid-intracellular-tutorial||rel=" noopener noreferrer" target="_blank"]] ===
abonard 199.1 68
abonard 200.1 69 **Level**: advanced(%%) **Type**: interactive tutorial
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71 This tutorial provides an overview of how to set up a simple travelling wave in both cases.
abonard 201.1 72 === [[Reaction-Diffusion – Initialization strategies>>https://neuron.yale.edu/neuron/docs/initialization-strategies||rel=" noopener noreferrer" target="_blank"]] ===
abonard 200.1 73
abonard 201.1 74 **Level**: advanced(%%) **Type**: interactive tutorial
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76 In this tutorial you will learn how to implement cell signalling function in the reaction-diffusion system by characterising your problems by the answers to three questions: (1) Where do the dynamics occur, (2) Who are the actors, and (3) How do they interact?
abonard 202.1 77 === [[Ball and Stick model part 3>>https://neuron.yale.edu/neuron/docs/ball-and-stick-model-part-3||rel=" noopener noreferrer" target="_blank"]] ===
abonard 201.1 78
abonard 202.1 79 **Level**: advanced(%%) **Type**: user documentation
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abonard 203.1 81 === [[Using the CellBuilder – Introduction>>https://neuron.yale.edu/neuron/static/docs/cbtut/main.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 202.1 82
abonard 203.1 83 **Level**: advanced(%%) **Type**: interactive tutorial
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85 The following tutorials show how to use the CellBuilder, a powerful and convenient tool for constructing and managing models of individual neurons. It breaks the job of model specification into a sequence of tasks:
86 1. Setting up model topology (branching pattern).
87 2. Grouping sections with shared properties into subsets.
88 3. Assigning geometric properties (length, diameter) to subsets or individual sections, and specifying a discretization strategy (i.e. how to set nseg).
89 4. Assigning biophysical properties (Ra, cm, ion channels, buffers, pumps, etc.) to subsets or individual sections.
abonard 204.1 90 === [[Using Import3D – Exploring morphometric data and fixing problems>>https://neuron.yale.edu/neuron/docs/import3d/fix_problems||rel=" noopener noreferrer" target="_blank"]] ===
abonard 203.1 91
abonard 204.1 92 **Level**: advanced(%%) **Type**: user documentation
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94 Import3D tool can be used to translate common varieties of cellular morphometric data into a CellBuilder that specifies the anatomical properties of a model neuron. This Tutorial will guide you through how to fix problems in your morphometric data.
abonard 205.1 95 === [[Randomness in NEURON models– The solution>>https://neuron.yale.edu/neuron/docs/solution||rel=" noopener noreferrer" target="_blank"]] ===
abonard 204.1 96
abonard 205.1 97 **Level**: advanced(%%) **Type**: user documentation
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99 In this part of the tutorial we will show you how to give NetStim its own random number generator.
abonard 206.1 100 === [[Segmentation intro: Dealing with simulations that generate a lot of data>>https://neuron.yale.edu/neuron/docs/dealing-simulations-generate-lot-data||rel=" noopener noreferrer" target="_blank"]] ===
abonard 205.1 101
abonard 206.1 102 **Level**: advanced(%%) **Type**: user documentation
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104 How to deal with simulations that generate a lot of data that must be saved? We will showcase different approaches.
abonard 207.1 105 === [[Using the Channel Builder – Creating a channel from an HH-style specification>>https://neuron.yale.edu/neuron/static/docs/chanlbild/hhstyle/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 206.1 106
abonard 207.1 107 **Level**: advanced(%%) **Type**: interactive tutorial
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109 Our goal is to implement a new voltage-gated macroscopic current whose properties are described by HH-style equations.
abonard 208.1 110 === [[Using the Channel Builder – Creating a channel from a kinetic scheme specification>>https://neuron.yale.edu/neuron/static/docs/chanlbild/kinetic/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 207.1 111
abonard 208.1 112 **Level**: advanced(%%) **Type**: interactive tutorial
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114 Here we will implement a new voltage-gated macroscopic current whose properties are described by a family of chemical reactions.
abonard 209.1 115 === [[Randomness in NEURON models– Source code that demonstrates the solution>>https://neuron.yale.edu/neuron/docs/source-code-demonstrates-solution||rel=" noopener noreferrer" target="_blank"]] ===
abonard 208.1 116
abonard 209.1 117 **Level**: advanced(%%) **Type**: user documentation
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abonard 210.1 119 === [[Using the Network Builder – Introduction to Network Construction>>https://neuron.yale.edu/neuron/static/docs/netbuild/intro.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 209.1 120
abonard 210.1 121 **Level**: advanced(%%) **Type**: user documentation
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abonard 211.1 123 === [[Python introduction>>https://neuron.yale.edu/neuron/docs/python-introduction||rel=" noopener noreferrer" target="_blank"]] ===
abonard 210.1 124
abonard 211.1 125 **Level**: advanced(%%) **Type**: user documentation
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127 This page provides a brief introduction to Python syntax, Variables, Lists and Dicts, For loops and iterators, Functions, Classes, Importing modules, Writing and reading files with Pickling.
abonard 212.1 128 === [[Reaction-Diffusion Example – RxD with MOD files>>https://neuron.yale.edu/neuron/docs/rxd-mod-files||rel=" noopener noreferrer" target="_blank"]] ===
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abonard 212.1 130 **Level**: advanced(%%) **Type**: user documentation
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132 NEURON's reaction-diffusion infrastructure can be used to readily allow intracellular concentrations to respond to currents generated in MOD files. This example shows you a simple model with just a single point soma, of length and diameter 10 microns, with Hodgkin-Huxley kinetics, and dynamic sodium (declared using rxd but without any additional kinetics).
abonard 213.1 133 === [[Segmenting a simulation of a model network - Introduction>>https://neuron.yale.edu/neuron/docs/segmenting-simulation-model-network||rel=" noopener noreferrer" target="_blank"]] ===
abonard 212.1 134
abonard 213.1 135 **Level**: advanced(%%) **Type**: user documentation
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abonard 214.1 137 === [[Using the Network Builder – Tutorial 1: Making Networks of Artificial Neurons>>https://neuron.yale.edu/neuron/static/docs/netbuild/artnet/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
abonard 213.1 138
abonard 214.1 139 **Level**: advanced(%%) **Type**: interactive tutorial
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141 Learn how to Artificial Integrate and Fire cell with a synapse that is driven by an afferent burst of spikes.
abonard 215.1 142 === [[Reaction-Diffusion Example – Restricting a reaction to part of a region>>https://neuron.yale.edu/neuron/docs/example-restricting-reaction-part-region||rel=" noopener noreferrer" target="_blank"]] ===
abonard 214.1 143
abonard 215.1 144 **Level**: advanced(%%) **Type**: user documentation
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146 Implementation example for the restriction of the reaction to part of a region.
abonard 216.1 147 === [[Segmenting a simulation of a model cell - Introduction>>https://neuron.yale.edu/neuron/docs/segmenting-simulation-model-cell||rel=" noopener noreferrer" target="_blank"]] ===
abonard 215.1 148
abonard 216.1 149 **Level**: advanced(%%) **Type**: user documentation
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abonard 217.1 151 === [[Scripting NEURON basics>>https://neuron.yale.edu/neuron/docs/scripting-neuron-basics||rel=" noopener noreferrer" target="_blank"]] ===
abonard 216.1 152
abonard 217.1 153 **Level**: advanced(%%) **Type**: user documentation
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155 The objectives of this part of the tutorial are to get familiar with basic operations of NEURON using Python. In this worksheet we will: Create a passive cell membrane in NEURON. Create a synaptic stimulus onto the neuron. Modify parameters of the membrane and stimulus. Visualize results with bokeh.
abonard 218.1 156 === [[Reaction-Diffusion – Thresholds>>https://neuron.yale.edu/neuron/docs/reaction-diffusion-thresholds||rel=" noopener noreferrer" target="_blank"]] ===
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abonard 218.1 158 **Level**: advanced(%%) **Type**: interactive tutorial
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160 Learn how to scale reaction rates by a function of the form f(x) for suitably chosen a and m to approximately threshold them by a concentration.
abonard 219.1 161 === [[Randomness in NEURON models>>https://neuron.yale.edu/neuron/docs/randomness-neuron-models||rel=" noopener noreferrer" target="_blank"]] ===
abonard 218.1 162
abonard 219.1 163 **Level**: advanced(%%) **Type**: user documentation
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165 We will touch upon the following subjects in this tutorial:
166 How to create model specification code that employs randomization to avoid undesired correlations between parameters, and to produce a model cell or network that has the same architecture and biophysical properties, and generates the same simulation results regardless of whether it is run on serial or parallel hardware.
167 How to generate spike streams or other signals that fluctuate in ways that are statistically independent of each other.
168