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153	153	**Level**: advanced(%%) **Type**: user documentation
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155	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"]] ===
156	156
	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	+