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**Level**: advanced(%%) **Type**: user documentation |
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Implementation example for the restriction of the reaction to part of a region. |
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-=== [[Segmenting a simulation of a model cell - Introduction>>https://neuron.yale.edu/neuron/docs/segmenting-simulation-model-cell||rel=" noopener noreferrer" target="_blank"]] === |
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-**Level**: advanced(%%) **Type**: user documentation |
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-=== [[Scripting NEURON basics>>https://neuron.yale.edu/neuron/docs/scripting-neuron-basics||rel=" noopener noreferrer" target="_blank"]] === |
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-**Level**: advanced(%%) **Type**: user documentation |
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-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. |
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-=== [[Reaction-Diffusion – Thresholds>>https://neuron.yale.edu/neuron/docs/reaction-diffusion-thresholds||rel=" noopener noreferrer" target="_blank"]] === |
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-**Level**: advanced(%%) **Type**: interactive tutorial |
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-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. |
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-=== [[Randomness in NEURON models>>https://neuron.yale.edu/neuron/docs/randomness-neuron-models||rel=" noopener noreferrer" target="_blank"]] === |
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-**Level**: advanced(%%) **Type**: user documentation |
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-We will touch upon the following subjects in this tutorial: |
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-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. |
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-How to generate spike streams or other signals that fluctuate in ways that are statistically independent of each other. |
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