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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	+