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Changes for page Neuron

Last modified by abonard on 2025/09/16 10:47
From version 218.1
edited by abonard
on 2025/06/03 11:05
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To version 324.1
edited by abonard
on 2025/09/16 10:47
Change comment: There is no comment for this version

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69 69  **Level**: advanced(%%) **Type**: interactive tutorial
70 70  
71 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 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 -