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Merge pull request #3428 from jessica-mitchell/network-docs
Improve discoverablility of network models in NEST docs
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doc/htmldoc/examples/index.rst

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===============
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.. grid:: 1 1 2 3
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:gutter: 1
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.. grid-item-card:: Simple networks
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:img-top: ../static/img/pynest/mc_neuron.png
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* :doc:`../auto_examples/brunel_alpha_evolution_strategies`
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.. grid-item-card:: Cortical microcircuit (Potjans)
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:img-top: ../auto_examples/Potjans_2014/potjans_2014_raster_plot.png
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* :doc:`../auto_examples/Potjans_2014/index`
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.. grid-item-card:: Large scale cortical network models
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:img-top: ../static/img/network_model_sketch_mesocircuit.png
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* :doc:`../auto_examples/Potjans_2014/index` (Potjans and Diesmann 2014)
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* `The mesocircuit model <https://mesocircuit-model.readthedocs.io/en/latest/>`_: A layered cortical network with distance-dependent connectivity
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* `The multi-area model <https://inm-6.github.io/multi-area-model/>`_: A network model of the visual cortex of the macaque monkey
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* `The HuMAM model <https://github.com/INM-6/human-multi-area-model>`_: Multi-Scale Spiking Network Model of Human Cerebral Cortex
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.. grid-item-card:: EI clustered network (Rostami et al)
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* :doc:`../auto_examples/wang_decision_making`
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.. grid:: 1 1 2 3
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.. grid-item-card:: Gap junctions

doc/htmldoc/get-started_index.rst

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* :doc:`Brunel network </auto_examples/brunel_alpha_nest>`: A random balanced network
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* :doc:`The microcircuit model <../auto_examples/Potjans_2014/index>`: A network model example based on Potjans and Diesman 2014
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* `The mesocircuit model <https://mesocircuit-model.readthedocs.io/en/latest/>`_: A layered cortical network with distance-dependent connectivity
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* The `multi-area model <https://inm-6.github.io/multi-area-model/>`_: A network model of the visual cortex of the macaque monkey
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* `The multi-area model <https://inm-6.github.io/multi-area-model/>`_: A network model of the visual cortex of the macaque monkey
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doc/htmldoc/index.rst

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:data-bs-ride: carousel
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.. figure:: static/img/network_model_sketch_mesocircuit.png
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:target: networks/index.html
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Create spatially structured networks
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Create large network models
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.. figure:: static/img/astrocyte_interaction.png
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:target: auto_examples/astrocytes/index.html
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Inspect neuron and astrocyte interactions
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.. figure:: static/img/hpc_benchmark_connectivity.svg
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:target: auto_examples/hpc_benchmark.html
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Test perfomance and benchmarks
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.. figure:: static/img/pong_sim.gif
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:target: auto_examples/pong/run_simulations.html
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Simulate a game of PONG with NEST
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.. figure:: static/img/gapjunctions.png
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.. figure:: static/img/pynest/eprop_supervised_classification_infrastructure.png
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:target: auto_examples/eprop_plasticity/index.html
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Explore synapse types like gap junctions
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Explore eligibility propagation plasticity
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.. grid-item::
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doc/htmldoc/models/index.rst

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===============
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What type of model?
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-------------------
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NEST has over 100 models, choose an option for finding the one you need!
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.. grid::
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.. grid-item-card::
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:columns: 4
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:link: network_models
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:link-type: ref
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**Network models**
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.. grid-item-card::
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:columns: 4
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:link: model_selector
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:link-type: ref
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**Neurons, synapses, and devices**
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:ref:`What's the difference? <model_meaning>`
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.. _model_selector:
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Model selector
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implementations of these conceptual entities. Most of the models in
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NEST are based on either peer-reviewed publications or text books
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like [1]_. This is what we mean for models in our model directory.
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Note that **devices** are not models but are mechanisms to generate or read out
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signals, like spikes. We list them together with neurons and synapses since they
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are required to be able to produce and analyze neuron and synapse activity.
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2. **Network models**. These models are created from individual neuron
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and synapse models using the different commands provided by the
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:ref:`PyNEST API <pynest_api>`. Examples for such network models
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can be found :doc:`here </examples/index>`.
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:ref:`PyNEST API <pynest_api>`. Network models have a defined population
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and connectivity, with initial conditions, along with specific neuron and synapse models.
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We have :doc:`a variety of examples for network models </examples/index>` and specifically,
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:doc:`large scale networks examples </networks/index>`.
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.. seealso::
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{% for keys in model_dict %}
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{{ keys | replace(".html", "") }}
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{% endfor %}
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.. toctree::
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:hidden:
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../networks/index

doc/htmldoc/networks/index.rst

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.. _network_models:
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Network Models
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==============
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Here we have several examples of large-scale network models developed for NEST.
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You can see how to build other networks for various neuron, synapse and device types in our
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:doc:`examples gallery </examples/index>`.
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We have a detailed guide on :ref:`spatially-structured networks <spatial_networks>` as well.
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.. grid:: 1 2 3 4
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:gutter: 1
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.. grid-item-card:: Brunel Network
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:columns: 4
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:link: /auto_examples/brunel_alpha_nest
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:link-type: doc
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A random balanced network
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.. grid-item-card:: Microcircuit Model
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:columns: 4
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:link: /auto_examples/Potjans_2014/index
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:link-type: doc
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A network model example based on Potjans and Diesman 2014
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.. grid-item-card:: Mesocircuit Model
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:columns: 4
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:link: https://mesocircuit-model.readthedocs.io/en/latest/
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A layered cortical network with distance-dependent connectivity
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.. grid-item-card:: Multi-area Model
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:columns: 4
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:link: https://inm-6.github.io/multi-area-model/
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A network model of the visual cortex of the macaque monkey
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.. grid-item-card:: HuMAM model
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:columns: 4
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:link: https://github.com/INM-6/human-multi-area-model
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Multi-Scale Spiking Network Model of Human Cerebral Cortex

doc/htmldoc/static/img/excalidraw_nestconcept_horiz.svg

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