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74	74	**Level**: advanced(%%) **Type**: interactive tutorial
75	75
76	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	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		-