Changes for page Neuron

Last modified by abonard on 2025/09/16 10:47
From version 295.1
edited by abonard
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To version 203.1
edited by abonard
on 2025/06/03 11:04
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87 87  2. Grouping sections with shared properties into subsets.
88 88  3. Assigning geometric properties (length, diameter) to subsets or individual sections, and specifying a discretization strategy (i.e. how to set nseg).
89 89  4. Assigning biophysical properties (Ra, cm, ion channels, buffers, pumps, etc.) to subsets or individual sections.
90 -=== [[Using Import3D – Exploring morphometric data and fixing problems>>https://neuron.yale.edu/neuron/docs/import3d/fix_problems||rel=" noopener noreferrer" target="_blank"]] ===
91 91  
92 -**Level**: advanced(%%) **Type**: user documentation
93 -
94 -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 through how to fix problems in your morphometric data.
95 -=== [[Randomness in NEURON models– The solution>>https://neuron.yale.edu/neuron/docs/solution||rel=" noopener noreferrer" target="_blank"]] ===
96 -
97 -**Level**: advanced(%%) **Type**: user documentation
98 -
99 -In this part of the tutorial we will show you how to give NetStim its own random number generator.
100 -=== [[Segmentation intro: Dealing with simulations that generate a lot of data>>https://neuron.yale.edu/neuron/docs/dealing-simulations-generate-lot-data||rel=" noopener noreferrer" target="_blank"]] ===
101 -
102 -**Level**: advanced(%%) **Type**: user documentation
103 -
104 -How to deal with simulations that generate a lot of data that must be saved? We will showcase different approaches.
105 -=== [[Using the Channel Builder – Creating a channel from an HH-style specification>>https://neuron.yale.edu/neuron/static/docs/chanlbild/hhstyle/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
106 -
107 -**Level**: advanced(%%) **Type**: interactive tutorial
108 -
109 -Our goal is to implement a new voltage-gated macroscopic current whose properties are described by HH-style equations.
110 -=== [[Using the Channel Builder – Creating a channel from a kinetic scheme specification>>https://neuron.yale.edu/neuron/static/docs/chanlbild/kinetic/outline.html||rel=" noopener noreferrer" target="_blank"]] ===
111 -
112 -**Level**: advanced(%%) **Type**: interactive tutorial
113 -
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"]] ===
116 -
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 -