| ... |
... |
@@ -78,46 +78,4 @@ |
| 78 |
78 |
|
| 79 |
79 |
**Level**: advanced(%%) **Type**: user documentation |
| 80 |
80 |
|
| 81 |
|
-=== [[Using the CellBuilder – Introduction>>https://neuron.yale.edu/neuron/static/docs/cbtut/main.html||rel=" noopener noreferrer" target="_blank"]] === |
| 82 |
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 |
|
- |
