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212	212	**Level**: advanced(%%) **Type**: user documentation
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	214	+=== [[Reaction-Diffusion Example – Circadian rhythm>>https://neuron.yale.edu/neuron/docs/example-circadian-rhythm||rel=" noopener noreferrer" target="_blank"]] ===
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	216	+**Level**: advanced(%%) **Type**: user documentation
	217	+
	218	+Here we develop a NEURON implementation of the Leloup-Goldbeter model for circadian rhythms in Drosophila. In this example NEURON's h library and its standard run system are being used as well as matplotlib to plot concentrations of circadian proteins over time.
	219	+=== [[Segmenting a simulation of a model cell – 3. Run a segmented simulation and save its results>>https://neuron.yale.edu/neuron/docs/3-run-segmented-simulation-and-save-its-results||rel=" noopener noreferrer" target="_blank"]] ===
	220	+
	221	+**Level**: advanced(%%) **Type**: user documentation
	222	+
	223	+=== [[ModelView: Compact display of parameters for NEURON models.>>https://neuron.yale.edu/neuron/static/papers/mview/modelviewhbp2004.html||rel=" noopener noreferrer" target="_blank"]] ===
	224	+
	225	+**Level**: advanced(%%) **Type**: user documentation
	226	+
	227	+This example demonstrates how ModelView can explore a NEURON model.
	228	+=== [[Segmenting a simulation of a model network – 3. Run a segmented simulation and save its results>>https://neuron.yale.edu/neuron/docs/3-run-segmented-simulation-and-save-its-results-0||rel=" noopener noreferrer" target="_blank"]] ===
	229	+
	230	+**Level**: advanced(%%) **Type**: user documentation
	231	+
	232	+=== [[Segmenting a simulation of a model network – 4. Reconstitute and verify the "complete" simulation results>>https://neuron.yale.edu/neuron/docs/4-reconstitute-and-verify-complete-simulation-results-0||rel=" noopener noreferrer" target="_blank"]] ===
	233	+
	234	+**Level**: advanced(%%) **Type**: user documentation
	235	+
	236	+=== [[Using NEURON's Optimization Tools – Tutorial 1 : Fitting a function to data>>https://neuron.yale.edu/neuron/static/docs/optimiz/func/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
	237	+
	238	+**Level**: advanced(%%) **Type**: user documentation
	239	+
	240	+We will look into how to bring up a Multiple Run Fitter, load the Experimental Data into the Multiple Run Fitter, specify the function we want to optimize, specify the parameters that will be adjusted, specify the criteria we want the function to satisfy and finally perform the optimization.
	241	+=== [[Ball and Stick model part 4>>https://neuron.yale.edu/neuron/docs/ball-and-stick-model-part-4||rel=" noopener noreferrer" target="_blank"]] ===
	242	+
	243	+**Level**: advanced(%%) **Type**: user documentation
	244	+
	245	+=== [[Reaction-Diffusion>>https://neuron.yale.edu/neuron/docs/reaction-diffusion||rel=" noopener noreferrer" target="_blank"]] ===
	246	+
	247	+**Level**: advanced(%%) **Type**: interactive tutorial
	248	+
	249	+In this tutorial you will learn how to implement cell signalling function in the reaction-diffusion system by characterising your problems by the answers to three questions: (1) Where do the dynamics occur, (2) Who are the actors, and (3) How do they interact?
	250	+=== [[Creating a stylized ("stick figure") model cell>>https://neuron.yale.edu/neuron/static/docs/cbtut/stylized/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
	251	+
	252	+**Level**: advanced(%%) **Type**: user documentation
	253	+
	254	+Our goal in this tutorial is to build an extremely simplified model of a pyramidal cell using the CellBuilder, a powerful and convenient tool for constructing and managing models of individual neurons. We will be looking into setting up model topology, grouping sections with shared properties into subsets, assigning geometric properties to subsets or individual sections, and specifying a discretization strategy, as well as assigning biophysical properties to subsets or individual sections.
	255	+=== [[Randomness in NEURON models– Source code that demonstrates the problem>>https://neuron.yale.edu/neuron/docs/source-code-demonstrates-problem||rel=" noopener noreferrer" target="_blank"]] ===
	256	+
	257	+**Level**: advanced(%%) **Type**: user documentation
	258	+
	259	+The tutorial will show you how to declare important constants (model parameters and simulation parameters), load files that other stuff will depend on, create the model itself (just a collection of cells that spike at random times), specify instrumentation (in this case, recording of spike times), specify simulation control and execute one or more simulations with various model parameters in the source code.
	260	+=== [[Using the Channel Builder – Creating a model of stochastic channel gating>>https://neuron.yale.edu/neuron/static/docs/chanlbild/stochastic/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
	261	+
	262	+**Level**: advanced(%%) **Type**: interactive tutorial
	263	+
	264	+Given a Channel Builder that implements a deterministic channel specified by a kinetic scheme, we create a new one that implements stochastic gating.
	265	+