neuronsimulator · heerener · Jul 6, 2023 · Jul 11, 2024 · Jul 15, 2024 · Jul 17, 2024
diff --git a/docs/courses/mpi_parallelization.rst b/docs/courses/mpi_parallelization.rst
@@ -38,11 +38,11 @@ The NEURON setup installer has already pu all the required sofware on your machi
 Mac OS X and Linux
 +++++++++++
 
-Unfortunately MPI can't be a part of the binary installation because I don't know if, which, or where MPI was installed on your machine. So you have to install MPI yourself, check that it works, and build NEURON from the sources with the configure option '--with-paranrn'. See the "installing and testing MPI" section of the Hines and Carnevale (2008) paper, "Translating network models to parallel hardware in NEURON", J. Neurosci. Meth. 169: 425-465. The paper is reprinted in your handout booklet. Or see `the ModelDB entry <https://senselab.med.yale.edu/ModelDB/ShowModel?model=96444#tabs-1>`_
+Unfortunately MPI can't be a part of the binary installation because I don't know if, which, or where MPI was installed on your machine. So you have to install MPI yourself, check that it works, and build NEURON from the sources with the configure option '--with-paranrn'. See the "installing and testing MPI" section of the Hines and Carnevale (2008) paper, "Translating network models to parallel hardware in NEURON", J. Neurosci. Meth. 169: 425-465. The paper is reprinted in your handout booklet. Or see `the ModelDB entry <https://modeldb.science/96444>`_
 
 Going Further 
 ----------
 
-The ring model from the `above ModelDB entry <https://senselab.med.yale.edu/ModelDB/ShowModel?model=96444#tabs-1>`_ is a good next step. See also the documentation for the `ParallelContext <https://nrn.readthedocs.io/en/latest/hoc/modelspec/programmatic/network/parcon.html?highlight=parallelcontext>`_ class, especialy the subset of methods gathered under the `ParallelNetwork <https://nrn.readthedocs.io/en/latest/hoc/modelspec/programmatic/network/parcon.html?highlight=parallelcontext>`_ heading. A large portion of the `ParallelNetManager <https://nrn.readthedocs.io/en/latest/hoc/modelspec/programmatic/network/parnet.html?highlight=parallelnetmanager>`_ wrapper is better off done directly from the underlying ParallelContext though it can be mined for interesting pieces. A good place to find the most recent idioms is the NEURON implementation of the Vogels and Abbott model found in the `Brette et al. ModelDB entry <https://senselab.med.yale.edu/ModelDB/ShowModel?model=83319#tabs-1>`_. However, to run in parallel, the NetCon delay between cells needs to be set greater than zero.
+The ring model from the `above ModelDB entry <https://modeldb.science/96444>`_ is a good next step. See also the documentation for the `ParallelContext <https://nrn.readthedocs.io/en/latest/hoc/modelspec/programmatic/network/parcon.html?highlight=parallelcontext>`_ class, especialy the subset of methods gathered under the `ParallelNetwork <https://nrn.readthedocs.io/en/latest/hoc/modelspec/programmatic/network/parcon.html?highlight=parallelcontext>`_ heading. A large portion of the `ParallelNetManager <https://nrn.readthedocs.io/en/latest/hoc/modelspec/programmatic/network/parnet.html?highlight=parallelnetmanager>`_ wrapper is better off done directly from the underlying ParallelContext though it can be mined for interesting pieces. A good place to find the most recent idioms is the NEURON implementation of the Vogels and Abbott model found in the `Brette et al. ModelDB entry <https://modeldb.science/83319>`_. However, to run in parallel, the NetCon delay between cells needs to be set greater than zero.
 
 
diff --git a/docs/courses/multithread_parallelization.rst b/docs/courses/multithread_parallelization.rst
@@ -13,7 +13,7 @@ Purkinje Cell
 Model
 -----
 
-`Miyasho et al. 2001 <https://modeldb.yale.edu/17664>`_
+`Miyasho et al. 2001 <https://modeldb.science/17664>`_
 
 Simulation
 ----------
@@ -86,7 +86,7 @@ Cortex integrates sensory information. What is a moment in time?
 Model
 -----
 
-Transient synchrony. `Hopfield and Brody 2001 <https://modeldb.yale.edu/2798>`_ implemented by Michele Migliore.
+Transient synchrony. `Hopfield and Brody 2001 <https://modeldb.science/2798>`_ implemented by Michele Migliore.
 
 Simulation
 ----------

diff --git a/docs/courses/neuron_scripting_exercises.rst b/docs/courses/neuron_scripting_exercises.rst
@@ -78,7 +78,7 @@ Connect the two cells with a gap junction (see :ref:`halfgap.mod <halfgap_mod_sc
 Exercise 5
 ----------
 
-Download the model from http://modeldb.yale.edu/126814 Create a Python script that loads the model, injects current into the center of the soma from t=2 to t=4 ms sufficient to generate an action potential, and records and plots membrane potential, sodium current, and potassium current as functions of time from t=0 to t=10 ms.
+Download the model from https://modeldb.science/126814 Create a Python script that loads the model, injects current into the center of the soma from t=2 to t=4 ms sufficient to generate an action potential, and records and plots membrane potential, sodium current, and potassium current as functions of time from t=0 to t=10 ms.
 
 *Hint*: ``h.load_file('mosinit.hoc')``
 

diff --git a/docs/courses/using_modeldb_and_modelview.rst b/docs/courses/using_modeldb_and_modelview.rst
@@ -3,7 +3,7 @@
 Using ModelDB with NEURON
 =========================
 
-`https://modeldb.yale.edu/ <https://modeldb.yale.edu/>`_ is the the home page of ModelDB.
+`https://modeldb.science <https://modeldb.science>`_ is the the home page of ModelDB.
 
 We'll be analyzing a couple of models in order to answer these questions:
 
@@ -16,7 +16,7 @@ We'll be analyzing a couple of models in order to answer these questions:
 3.
     What is the user interface, how was it implemented, and how do you use it?
 
-Example: Moore et al. 1983 `modeldb.yale.edu/9852 <https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=9852#tabs-1>`_
+Example: Moore et al. 1983 `modeldb.science/9852 <https://modeldb.science/9852>`_
 ------------------------------------------------------------------------------------------------------------------------------
 
     Moore JW, Stockbridge N, Westerfield M (1983)
@@ -170,12 +170,12 @@ Why does the space plot automatically save traces every 0.1 ms?
 What procedure actually changes the stimulus location, duration, and amplitude? Read about PointProcessManager in the help files.
 
 
-Another example: Mainen and Sejnowski 1996 `modeldb.yale.edu/2488 <https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=2488#tabs-1>`_
+Another example: Mainen and Sejnowski 1996 `modeldb.science/2488 <https://modeldb.science/2488>`_
 ----------------------------------------------------------------------------------------------------------------------------------------------
 
     Mainen ZF, Sejnowski TJ (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. *Nature* 382:363-6. doi: `doi.org/10.1038/382363a0 <https://www.nature.com/articles/382363a0#citeas>`_
 
-This one has interesting anatomy and several mod files. Begin by downloading the model from `modeldb.yale.edu/2488 <https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=2488#tabs-1>`_
+This one has interesting anatomy and several mod files. Begin by downloading the model from `modeldb.science/2488 <https://modeldb.science/2488>`_
 
 The model archive patdemo.zip has already been downloaded and unzipped. Its contents are in :file:`exercises/modeldb_and_modelview/patdemo`
 

diff --git a/docs/courses/using_nsg_portal.rst b/docs/courses/using_nsg_portal.rst
@@ -8,7 +8,7 @@ You can do this exercise on your own, or you might prefer to form a team of 2-4
 On your own computer:
 ---------------------
 
-Whether you're working on your own or in a team, everyone should start by downloading the zip file for the `Jones et al. 2009 model <https://modeldb.yale.edu/136803>`_ from ModelDB. Also get a copy of their paper and examine it to discover what is being modeled and what results are produced.
+Whether you're working on your own or in a team, everyone should start by downloading the zip file for the `Jones et al. 2009 model <https://modeldb.science/136803>`_ from ModelDB. Also get a copy of their paper and examine it to discover what is being modeled and what results are produced.
 
 Expand the zip file and examine its contents to figure out how to run a simulation (look for a "readme" file).
 

diff --git a/docs/guide/how_to_get_started.rst b/docs/guide/how_to_get_started.rst
@@ -21,13 +21,13 @@ Basic NEURON Usage
 * Use the `Programmer's Reference <../python/index.html>`_ early and often. 
 * Work through the tutorials on the Documentation and Courses pages.
 * Use the GUI tools as much as possible. You'll get more done, faster, and you won't have to write any code. Some of the GUI tools are described in the tutorials; others are demoed in the :ref:`course videos <training_videos>`. Save the GUI tools to session files; these files contain HOC and can be modified, adapted, and reused.
-* Examine `ModelDB <https://modeldb.yale.edu>`_ and the list of :ref:`publications about NEURON <publications_about_neuron>` to find models of interest. Many authors have deposited their model code in ModelDB, posted it somewhere else on the WWW, or will provide code upon request.
+* Examine `ModelDB <https://modeldb.science>`_ and the list of :ref:`publications about NEURON <publications_about_neuron>` to find models of interest. Many authors have deposited their model code in ModelDB, posted it somewhere else on the WWW, or will provide code upon request.
 
 Using NMODL to add new mechanisms to NEURON
 -------------------------------------------
 
 * First, consider using the ChannelBuilder instead. This is an extremely powerful GUI tool for specifying voltage- and ligand-gated ionic conductances. It's much easier to use than NMODL. Mechanisms specified with the ChannelBuilder actually execute faster than if they were specified with NMODL. Also, you can use it to make stochastic channel models.
 * If you absolutely must use NMODL (e.g. for ion accumulation mechanisms or to add new kinds of artificial spiking cells), read chapters 9 and 10 of The NEURON Book, or at least the articles "Expanding NEURON's Repertoire of Mechanisms with NMODL" and "Discrete event simulation in the NEURON environment".
 * NEURON comes with a bunch of mod files that can serve as starting points for "programming by example." Under MSWin the default mechanisms (hh, pas, expsyn etc.) are in `github.com/neuronsimulator/nrn/tree/master/src/nrnoc <https://github.com/neuronsimulator/nrn/tree/master/src/nrnoc>`_. A large collection of mod files is at `github.com/neuronsimulator/nrn/tree/master/share/examples/nrniv/nmodl <https://github.com/neuronsimulator/nrn/tree/master/share/examples/nrniv/nmodl>`_.
-* You may also find useful examples in `ModelDB <https://modeldb.yale.edu>`_.
+* You may also find useful examples in `ModelDB <https://modeldb.science>`_.
 
diff --git a/docs/guide/how_to_get_started_with_neuron.rst b/docs/guide/how_to_get_started_with_neuron.rst
@@ -26,7 +26,7 @@ Then
     The GUI tools can also help you learn how to use hoc, NEURON's programming language. The CellBuilder and Network Builder can export hoc code that you can examine and reuse to do new things. You can also save the smaller GUI tools to session files, which contain reusable hoc statements.
 
 6.
-    Examine `ModelDB <https://senselab.med.yale.edu/modeldb/>`_ and the list of `publications about NEURON <https://nrn.readthedocs.io/en/latest/publications.html>`_ to find models of interest. Many authors have deposited their model code in ModelDB, posted it somewhere else on the WWW, or will provide code upon request.
+    Examine `ModelDB <https://modeldb.science>`_ and the list of `publications about NEURON <https://nrn.readthedocs.io/en/latest/publications.html>`_ to find models of interest. Many authors have deposited their model code in ModelDB, posted it somewhere else on the WWW, or will provide code upon request.
 
 To learn how to use NMODL to add new mechanisms to NEURON:
 ------------------------
@@ -41,7 +41,7 @@ To learn how to use NMODL to add new mechanisms to NEURON:
     NEURON comes with a bunch of mod files that can serve as starting points for "programming by example." Under MSWin the default mechanisms (hh, pas, expsyn etc.) are in ``c:\nrn\src\nrnoc`` (on my Linux box this is ``/usr/local/src/nrn-x.x/src/nrnoc``). A large collection of mod files is in ``c:\nrn\examples\nrniv\nmodl`` (Linux ``/usr/local/src/nrn-x.x/share/examples/nrniv/nmodl``).
 
 4.
-    You may also find useful examples in `ModelDB <https://senselab.med.yale.edu/modeldb/>`_.
+    You may also find useful examples in `ModelDB <https://modeldb.science>`_.
 
 For courses about NEURON, see the :ref:`Course Exercises <exercises2018>` page and the :ref:`Training Videos <training_videos>` page.
 

diff --git a/docs/guide/introduction_to_network_construction.rst b/docs/guide/introduction_to_network_construction.rst
@@ -57,4 +57,4 @@ It's a good idea to use artificial neurons for prototyping networks for two reas
 
 Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, et al. (2007) Simulation of networks of spiking neurons: A review of tools and strategies. J Comp Neurosci 23:349-98.
 
-`Source code available from ModelDB via accession number 83319. <https://senselab.med.yale.edu/ModelDB/ShowModel?model=83319#tabs-1>`_
+`Source code available from ModelDB via accession number 83319. <https://modeldb.science/83319>`_
diff --git a/docs/guide/modelview_compact_display.rst b/docs/guide/modelview_compact_display.rst
@@ -12,7 +12,7 @@ ModelView: Compact display of parameters for NEURON models
 Introduction
 ------------
 
-The availability of a large number of models in `ModelDB <https://senselab.med.yale.edu/ModelDB/>`_ (Migliore et al 2003), helps investigators test their intuition of model behavior and provides building blocks for future modeling applications to the interpretation of experimental findings. However the NEURON (Hines and Carnevale 2001) model legacy code entered by publication authors was generally not developed with presentation as a high priority. The original code can be difficult to analyze and it sometimes happens that variables are reset so that the values at run time are different than the first values indicated in the top of the code. ModelView overcomes these problems by providing a (run-time state) preview of the properties of a model (anatomy and biophysical attributes). Having this information available for viewing in ModelDB lets investigators quickly develop a conceptual picture of the model structure and compare parameter differences between runs. It makes it possible to ask detailed questions about the model that would have been time-consuming to answer without ModelView.
+The availability of a large number of models in `ModelDB <https://modeldb.science>`_ (Migliore et al 2003), helps investigators test their intuition of model behavior and provides building blocks for future modeling applications to the interpretation of experimental findings. However the NEURON (Hines and Carnevale 2001) model legacy code entered by publication authors was generally not developed with presentation as a high priority. The original code can be difficult to analyze and it sometimes happens that variables are reset so that the values at run time are different than the first values indicated in the top of the code. ModelView overcomes these problems by providing a (run-time state) preview of the properties of a model (anatomy and biophysical attributes). Having this information available for viewing in ModelDB lets investigators quickly develop a conceptual picture of the model structure and compare parameter differences between runs. It makes it possible to ask detailed questions about the model that would have been time-consuming to answer without ModelView.
 
 ModelView in action 
 ---------

diff --git a/docs/guide/randomness.rst b/docs/guide/randomness.rst
@@ -20,14 +20,14 @@ These papers present models implemented with NEURON that provide some examples o
 
         Carnevale NT, Hines ML. (2008). Translating network models to parallel hardware in NEURON. *Journal of neuroscience methods*, 169(2), 425. `doi:10.1016/j.jneumeth.2007.09.010 <https://doi.org/10.1016/j.jneumeth.2007.09.010>`_
 
-        Preprint available from http://www.neuron.yale.edu/neuron/nrnpubs/, source code available via accession number 96444 from ModelDB http://modeldb.yale.edu/96444
+        Preprint available from http://www.neuron.yale.edu/neuron/nrnpubs/, source code available via accession number 96444 from ModelDB http://modeldb.science/96444
 
 *
     The network models in
 
         Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., ...  Destexhe, A. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. *Journal of computational neuroscience*, 23(3), 349-398. `doi:10.1007/s10827-007-0038-6 <https://doi.org/10.1007/s10827-007-0038-6>`_
 
-        Preprint available from http://www.neuron.yale.edu/neuron/nrnpubs/, source code available via accession number 83319 from ModelDB http://modeldb.yale.edu/83319
+        Preprint available from http://www.neuron.yale.edu/neuron/nrnpubs/, source code available via accession number 83319 from ModelDB http://modeldb.science/83319
 
 Interested users are encouraged to read these papers, and download and analyze the related source code, in order to better understand how to apply randomization in the construction and simulation of models.
 

diff --git a/docs/guide/what_is_neuron.rst b/docs/guide/what_is_neuron.rst
@@ -90,7 +90,7 @@ For some examples of how NEURON can be used, be sure to see Demonstrations and e
 User-extendable library of biophysical mechanisms
 -------------------------------------------------
 
-User-defined mechanisms such as voltage- and ligand-gated ion channels, diffusion, buffering, active transport, etc., can be added by writing model descriptions in NMODL, a high-level programming language that has a simple syntax for expressing kinetic schemes and sets of simultaneous algebraic and/or differential equations. NMODL can also be used to write model descriptions for new classes of artificial spiking cells. These model descriptions are compiled so that membrane voltage and states can be computed efficiently using integration methods that have been optimized for branched structures. A large number of mechanisms written in NMODL have been made available on the WWW by the authors of published models; many of these have been entered into `ModelDB <https://modeldb.yale.edu>`_ which makes it easy for users to find and retrieve model source code according to search criteria such as author, type of model (e.g. cell or network), ionic currents, etc..
+User-defined mechanisms such as voltage- and ligand-gated ion channels, diffusion, buffering, active transport, etc., can be added by writing model descriptions in NMODL, a high-level programming language that has a simple syntax for expressing kinetic schemes and sets of simultaneous algebraic and/or differential equations. NMODL can also be used to write model descriptions for new classes of artificial spiking cells. These model descriptions are compiled so that membrane voltage and states can be computed efficiently using integration methods that have been optimized for branched structures. A large number of mechanisms written in NMODL have been made available on the WWW by the authors of published models; many of these have been entered into `ModelDB <https://modeldb.science>`_ which makes it easy for users to find and retrieve model source code according to search criteria such as author, type of model (e.g. cell or network), ionic currents, etc..
 
 As mentioned above, NEURON also has a GUI tool called the ChannelBuilder that makes it easy to specify voltage- and ligand-gated ion channels in terms of ODEs (HH-style, including Borg-Graham formulation) and/or kinetic schemes. ChannelBuilder channels actually execute faster than identical mechanisms specified with NMODL. Their states and total conductance can be simulated as deterministic (continuous in time), or stochastic (countably many channels with independent state transitions, producing random, abrupt conductance changes).
Original file line number	Diff line number	Diff line change
Expand Up		@@ -57,4 +57,4 @@ It's a good idea to use artificial neurons for prototyping networks for two reas

		Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, et al. (2007) Simulation of networks of spiking neurons: A review of tools and strategies. J Comp Neurosci 23:349-98.

		`Source code available from ModelDB via accession number 83319. <https://senselab.med.yale.edu/ModelDB/ShowModel?model=83319#tabs-1>`_
		`Source code available from ModelDB via accession number 83319. <https://modeldb.science/83319>`_