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112	112	**Level**: advanced(%%) **Type**: interactive tutorial
113	113
114	114	Here we will implement a new voltage-gated macroscopic current whose properties are described by a family of chemical reactions.
	115	+=== [[Randomness in NEURON models– Source code that demonstrates the solution>>https://neuron.yale.edu/neuron/docs/source-code-demonstrates-solution||rel=" noopener noreferrer" target="_blank"]] ===
115	115
	117	+**Level**: advanced(%%) **Type**: user documentation
	118	+
	119	+=== [[Using the Network Builder – Introduction to Network Construction>>https://neuron.yale.edu/neuron/static/docs/netbuild/intro.html||rel=" noopener noreferrer" target="_blank"]] ===
	120	+
	121	+**Level**: advanced(%%) **Type**: user documentation
	122	+
	123	+=== [[Python introduction>>https://neuron.yale.edu/neuron/docs/python-introduction||rel=" noopener noreferrer" target="_blank"]] ===
	124	+
	125	+**Level**: advanced(%%) **Type**: user documentation
	126	+
	127	+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.
	128	+=== [[Reaction-Diffusion Example – RxD with MOD files>>https://neuron.yale.edu/neuron/docs/rxd-mod-files||rel=" noopener noreferrer" target="_blank"]] ===
	129	+
	130	+**Level**: advanced(%%) **Type**: user documentation
	131	+
	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	+
	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	+=== [[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	+=== [[Using Import3D – An introduction>>https://neuron.yale.edu/neuron/docs/import3d||rel=" noopener noreferrer" target="_blank"]] ===
	174	+
	175	+**Level**: advanced(%%) **Type**: user documentation
	176	+
	177	+Import3D tool can be used to translate common varieties of cellular morphometric data into a CellBuilder that specifies the anatomical properties of a model neuron. This Tutorial will guide you in reading a morphometric data file and converting it to a NEURON model as well as
	178	+exploring morphometric data and fixing problems.
	179	+=== [[Segmenting a simulation of a model network – 1. Implement and test the computational model itself>>https://neuron.yale.edu/neuron/docs/1-implement-and-test-computational-model-itself-0||rel=" noopener noreferrer" target="_blank"]] ===
	180	+
	181	+**Level**: advanced(%%) **Type**: user documentation
	182	+
	183	+=== [[Segmenting a simulation of a model network – 2. Run a "complete" simulation and save its results>>https://neuron.yale.edu/neuron/docs/2-run-complete-simulation-and-save-its-results-0||rel=" noopener noreferrer" target="_blank"]] ===
	184	+
	185	+**Level**: advanced(%%) **Type**: user documentation
	186	+
	187	+=== [[Segmenting a simulation of a model cell – 2. Run a "complete" simulation and save its results>>https://neuron.yale.edu/neuron/docs/2-run-complete-simulation-and-save-its-results||rel=" noopener noreferrer" target="_blank"]] ===
	188	+
	189	+**Level**: advanced(%%) **Type**: user documentation
	190	+
	191	+=== [[Segmenting a simulation of a model cell – 1. Implement and test the computational model itself>>https://neuron.yale.edu/neuron/docs/1-implement-and-test-computational-model-itself||rel=" noopener noreferrer" target="_blank"]] ===
	192	+
	193	+**Level**: advanced(%%) **Type**: user documentation
	194	+
	195	+=== [[Using NEURON's Optimization Tools – Tutorial 2 : Fitting a model to data>>https://neuron.yale.edu/neuron/static/docs/optimiz/model/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
	196	+
	197	+**Level**: advanced(%%) **Type**: user documentation
	198	+
	199	+We will go over how to create an "unoptimized" model, set up a current clamp experiment on this model, configure a MultipleRunFitter to do a "run fitness" optimization, load the Experimental Data into the iclamp Run Fitness Generator, specify the parameters that will be adjusted and finally perform the optimization.
	200	+=== [[Reaction-Diffusion – Hodgkin-Huxley using rxd>>https://neuron.yale.edu/neuron/docs/hodgkin-huxley-using-rxd||rel=" noopener noreferrer" target="_blank"]] ===
	201	+
	202	+**Level**: advanced(%%) **Type**: interactive tutorial
	203	+
	204	+In this tutorial you will learn how to set the proper parameters for the Hodgkin–Huxley model in NEURON.
	205	+=== [[Using the CellBuilder – Creating a stylised ("stick-figure") model cell>>https://neuron.yale.edu/neuron/static/docs/cbtut/stylized/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
	206	+
	207	+**Level**: advanced(%%) **Type**: -
	208	+
	209	+Learn how to build an extremely simplified model of a pyramidal cell.
	210	+=== [[Ball and Stick model part 2>>https://neuron.yale.edu/neuron/docs/ball-and-stick-model-part-2||rel=" noopener noreferrer" target="_blank"]] ===
	211	+
	212	+**Level**: advanced(%%) **Type**: user documentation
	213	+
	214	+=== [[Reaction-Diffusion Example – Circadian rhythm>>https://neuron.yale.edu/neuron/docs/example-circadian-rhythm||rel=" noopener noreferrer" target="_blank"]] ===
	215	+
	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	+