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139	139	**Level**: advanced(%%) **Type**: interactive tutorial
140	140
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"]] ===
143	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		-