Changes for page Neuron

Last modified by abonard on 2025/09/16 10:47
From version 257.1
edited by abonard
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To version 343.1
edited by abonard
on 2025/09/16 10:47
Change comment: There is no comment for this version

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2 2  
3 3  * ((( ==== **[[Beginner >>||anchor = "HBeginner-1"]]** ==== )))
4 4  
5 +* ((( ==== **[[Advanced >>||anchor = "HAdvanced-1"]]** ==== )))
6 +
5 5  === **Beginner** ===
6 6  
7 7  === [[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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40 40  **Level**: beginner(%%) **Type**: interactive tutorial
41 41  
42 42  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.
45 +=== [[The hoc programming language>>https://neuron.yale.edu/neuron/static/docs/programming/hoc_slides.pdf||rel=" noopener noreferrer" target="_blank"]] ===
43 43  
47 +**Level**: beginner(%%) **Type**: slide deck
48 +
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).
50 +=== [[A NEURON Programming Tutorial - Part E>>http://web.mit.edu/neuron_v7.4/nrntuthtml/tutorial/tutE.html||rel=" noopener noreferrer" target="_blank"]] ===
51 +
52 +**Level**: beginner(%%) **Type**: user documentation
53 +
54 +After this tutorial, students will be able to save data from the simulations and methods for increasing simulation speed.
55 +=== **Advanced** ===
56 +
57 +=== [[Reaction-Diffusion – Radial Diffusion>>https://neuron.yale.edu/neuron/docs/radial-diffusion||rel=" noopener noreferrer" target="_blank"]] ===
58 +
59 +**Level**: advanced(%%) **Type**: -
60 +
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.
62 +=== [[Reaction-Diffusion Example – Calcium Wave>>https://neuron.yale.edu/neuron/docs/reaction-diffusion-calcium-wave||rel=" noopener noreferrer" target="_blank"]] ===
63 +
64 +**Level**: advanced(%%) **Type**: interactive tutorial
65 +
66 +The model presented in this tutorial generates Ca2+ waves and is a simplification of the model we used in Neymotin et al., 2015.
67 +=== [[Reaction-Diffusion – 3D/Hybrid Intracellular Tutorial>>https://neuron.yale.edu/neuron/docs/3dhybrid-intracellular-tutorial||rel=" noopener noreferrer" target="_blank"]] ===
68 +
69 +**Level**: advanced(%%) **Type**: interactive tutorial
70 +
71 +This tutorial provides an overview of how to set up a simple travelling wave in both cases.
72 +=== [[Reaction-Diffusion – Initialization strategies>>https://neuron.yale.edu/neuron/docs/initialization-strategies||rel=" noopener noreferrer" target="_blank"]] ===
73 +
74 +**Level**: advanced(%%) **Type**: interactive tutorial
75 +
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?
77 +=== [[Ball and Stick model part 3>>https://neuron.yale.edu/neuron/docs/ball-and-stick-model-part-3||rel=" noopener noreferrer" target="_blank"]] ===
78 +
79 +**Level**: advanced(%%) **Type**: user documentation
80 +
81 +=== [[Using the CellBuilder – Introduction>>https://neuron.yale.edu/neuron/static/docs/cbtut/main.html||rel=" noopener noreferrer" target="_blank"]] ===
82 +
83 +**Level**: advanced(%%) **Type**: interactive tutorial
84 +
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.
90 +=== [[Using Import3D – Exploring morphometric data and fixing problems>>https://neuron.yale.edu/neuron/docs/import3d/fix_problems||rel=" noopener noreferrer" target="_blank"]] ===
91 +
92 +**Level**: advanced(%%) **Type**: user documentation
93 +
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.
95 +=== [[Randomness in NEURON models– The solution>>https://neuron.yale.edu/neuron/docs/solution||rel=" noopener noreferrer" target="_blank"]] ===
96 +
97 +**Level**: advanced(%%) **Type**: user documentation
98 +
99 +In this part of the tutorial we will show you how to give NetStim its own random number generator.
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"]] ===
101 +
102 +**Level**: advanced(%%) **Type**: user documentation
103 +
104 +How to deal with simulations that generate a lot of data that must be saved? We will showcase different approaches.
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"]] ===
106 +
107 +**Level**: advanced(%%) **Type**: interactive tutorial
108 +
109 +Our goal is to implement a new voltage-gated macroscopic current whose properties are described by HH-style equations.
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"]] ===
111 +
112 +**Level**: advanced(%%) **Type**: interactive tutorial
113 +
114 +Here we will implement a new voltage-gated macroscopic current whose properties are described by a family of chemical reactions.
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"]] ===
116 +
117 +**Level**: advanced(%%) **Type**: user documentation
118 +
119 +=== [[Using the Network Builder – Introduction to Network Construction>>https://neuron.yale.edu/neuron/static/docs/netbuild/intro.html||rel=" noopener noreferrer" target="_blank"]] ===
120 +
121 +**Level**: advanced(%%) **Type**: user documentation
122 +
123 +=== [[Python introduction>>https://neuron.yale.edu/neuron/docs/python-introduction||rel=" noopener noreferrer" target="_blank"]] ===
124 +
125 +**Level**: advanced(%%) **Type**: user documentation
126 +
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.
128 +=== [[Reaction-Diffusion Example – RxD with MOD files>>https://neuron.yale.edu/neuron/docs/rxd-mod-files||rel=" noopener noreferrer" target="_blank"]] ===
129 +
130 +**Level**: advanced(%%) **Type**: user documentation
131 +
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).
133 +=== [[Segmenting a simulation of a model network - Introduction>>https://neuron.yale.edu/neuron/docs/segmenting-simulation-model-network||rel=" noopener noreferrer" target="_blank"]] ===
134 +
135 +**Level**: advanced(%%) **Type**: user documentation
136 +
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"]] ===
138 +
139 +**Level**: advanced(%%) **Type**: interactive tutorial
140 +
141 +Learn how to Artificial Integrate and Fire cell with a synapse that is driven by an afferent burst of spikes.
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"]] ===
143 +
144 +**Level**: advanced(%%) **Type**: user documentation
145 +
146 +Implementation example for the restriction of the reaction to part of a region.
147 +=== [[Segmenting a simulation of a model cell - Introduction>>https://neuron.yale.edu/neuron/docs/segmenting-simulation-model-cell||rel=" noopener noreferrer" target="_blank"]] ===
148 +
149 +**Level**: advanced(%%) **Type**: user documentation
150 +
151 +=== [[Scripting NEURON basics>>https://neuron.yale.edu/neuron/docs/scripting-neuron-basics||rel=" noopener noreferrer" target="_blank"]] ===
152 +
153 +**Level**: advanced(%%) **Type**: user documentation
154 +
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.
156 +=== [[Reaction-Diffusion – Thresholds>>https://neuron.yale.edu/neuron/docs/reaction-diffusion-thresholds||rel=" noopener noreferrer" target="_blank"]] ===
157 +
158 +**Level**: advanced(%%) **Type**: interactive tutorial
159 +
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.
161 +=== [[Randomness in NEURON models>>https://neuron.yale.edu/neuron/docs/randomness-neuron-models||rel=" noopener noreferrer" target="_blank"]] ===
162 +
163 +**Level**: advanced(%%) **Type**: user documentation
164 +
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 +