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139	139	**Level**: advanced(%%) **Type**: interactive tutorial
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141	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"]] ===
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	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	+=== [[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"]] ===
	169	+
	170	+**Level**: advanced(%%) **Type**: interactive tutorial
	171	+
	172	+How to make one or more biophysical properties vary systematically with position in space.
	173	+