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**Level**: advanced(%%) **Type**: user documentation |
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+=== [[Python introduction>>https://neuron.yale.edu/neuron/docs/python-introduction||rel=" noopener noreferrer" target="_blank"]] === |
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+**Level**: advanced(%%) **Type**: user documentation |
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+This page provides a brief introduction to Python syntax, Variables, Lists and Dicts, For loops and iterators, Functions, Classes, Importing modules, Writing and reading files with Pickling. |
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+=== [[Reaction-Diffusion Example – RxD with MOD files>>https://neuron.yale.edu/neuron/docs/rxd-mod-files||rel=" noopener noreferrer" target="_blank"]] === |
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+**Level**: advanced(%%) **Type**: user documentation |
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+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). |
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+=== [[Segmenting a simulation of a model network - Introduction>>https://neuron.yale.edu/neuron/docs/segmenting-simulation-model-network||rel=" noopener noreferrer" target="_blank"]] === |
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+**Level**: advanced(%%) **Type**: user documentation |
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+=== [[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"]] === |
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+**Level**: advanced(%%) **Type**: interactive tutorial |
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+Learn how to Artificial Integrate and Fire cell with a synapse that is driven by an afferent burst of spikes. |
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+=== [[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"]] === |
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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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+=== [[Using the CellBuilder– Specifying parameterized variation of biophysical properties>>https://neuron.yale.edu/neuron/static/docs/cbtut/parameterized/outline.html||rel=" noopener noreferrer" target="_blank"]] === |
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+**Level**: advanced(%%) **Type**: interactive tutorial |
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+How to make one or more biophysical properties vary systematically with position in space. |
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