| ... |
... |
@@ -130,39 +130,4 @@ |
| 130 |
130 |
**Level**: advanced(%%) **Type**: user documentation |
| 131 |
131 |
|
| 132 |
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 |
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 |
|
- |
