| ... | ... | @@ -87,33 +87,4 @@ | 
              
                    | 87 | 87 | 2. Grouping sections with shared properties into subsets. | 
              
                    | 88 | 88 | 3. Assigning geometric properties (length, diameter) to subsets or individual sections, and specifying a discretization strategy (i.e. how to set nseg). | 
              
                    | 89 | 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 | 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 |  | - |