Add link to 3.7 whats new page in docs

doc/htmldoc/whats_new/index.rst

@@ -8,6 +8,7 @@ versions of NEST. On the linked pages, you will find both information about new
 features, as well as quick guides on how to transition your simulation code to
 the new versions.
 
+* :ref:`NEST 3.7 <release_3.7>`
 * :ref:`NEST 3.6 <release_3.6>`
 * :ref:`NEST 3.5 <release_3.5>`
 * :ref:`NEST 3.4 <release_3.4>`

doc/htmldoc/whats_new/v3.7/index.rst

@@ -13,13 +13,27 @@ If you transition from an earlier version, please see our extensive
 :ref:`transition guide from NEST 2.x to 3.0 <refguide_2_3>` and the
 :ref:`list of updates for previous releases in the 3.x series <whats_new>`.
 
+E-prop plasticity in NEST
+-------------------------
 
-NEST requires C++17
--------------------
+Another new NEST feature is eligibility propagation (e-prop) [1]_, a local and
+online learning algorithm for recurrent spiking neural networks (RSNNs) that
+serves as a biologically plausible approximation to backpropagation through time
+(BPTT). It relies on eligibility traces and neuron-specific learning signals to
+compute gradients without the need for error propagation backward in time. This
+approach aligns with the brain's learning mechanisms and offers a strong
+candidate for efficient training of RSNNs in low-power neuromorphic hardware.
+
+For further information, see:
+
+* :doc:`/auto_examples/eprop_plasticity/index`
+* :doc:`/models/index_e-prop plasticity`
+
+.. [1] Bellec G, Scherr F, Subramoney F, Hajek E, Salaj D, Legenstein R,
+       Maass W (2020). A solution to the learning dilemma for recurrent
+       networks of spiking neurons. Nature Communications, 11:3625.
+       https://doi.org/10.1038/s41467-020-17236-y
 
-From NEST 3.7 on, we use some C++17 features in NEST code. Therefore,
-NEST needs to be built with a compiler that supports C++17. Most
-recent C++ compilers should do so.
 
 Tripartite connectivity in NEST
 -------------------------------
@@ -42,23 +56,74 @@ See connectivity documentation:
 * :ref:`tripartite_connectivity`
 
 
-E-prop plasticity in NEST
--------------------------
+Ignore-and-fire neuron model
+----------------------------
 
-Another new NEST feature is eligibility propagation (e-prop) [1]_, a local and
-online learning algorithm for recurrent spiking neural networks (RSNNs) that
-serves as a biologically plausible approximation to backpropagation through time
-(BPTT). It relies on eligibility traces and neuron-specific learning signals to
-compute gradients without the need for error propagation backward in time. This
-approach aligns with the brain's learning mechanisms and offers a strong
-candidate for efficient training of RSNNs in low-power neuromorphic hardware.
+A neuron model for generating spikes at fixed intervals, irrespective of inputs.
 
-For further information, see:
+The ``ignore_and_fire`` neuron is primarily used for neuronal-network model verification and validation purposes
+("benchmarking"), in particular, to evaluate the correctness and performance of connectivity generation and inter-neuron
+communication. It permits an easy scaling of the network size and/or connectivity without affecting the output spike
+statistics.
 
-* :doc:`/auto_examples/eprop_plasticity/index`
-* :doc:`/models/index_e-prop plasticity`
+See documentation for more information:
 
-.. [1] Bellec G, Scherr F, Subramoney F, Hajek E, Salaj D, Legenstein R,
-       Maass W (2020). A solution to the learning dilemma for recurrent
-       networks of spiking neurons. Nature Communications, 11:3625.
-       https://doi.org/10.1038/s41467-020-17236-y
+* :doc:`/models/ignore_and_fire`
+
+Neuron model with integrated short-term plasticity
+--------------------------------------------------
+
+The new ``iaf_tum_2000`` neuron model incoroporates the ``tsodyks_synapse`` directly
+into the neuron model. In particular,
+``iaf_tum_2000`` implements short-term depression and short-term facilitation based on Tsodyks et al. [2]_.
+It is based on the ``iaf_psc_exp`` model.
+
+.. [2] Tsodyks M, Uziel A, Markram H (2000). Synchrony generation in recurrent
+       networks with frequency-dependent synapses. Journal of Neuroscience,
+       20 RC50. URL: http://infoscience.epfl.ch/record/183402
+
+
+New connection rule: ``pairwise_poisson``
+------------------------------------------
+
+The number of synapses between pre- and post-synaptic neurons is drawn from a Poisson distribution.
+The ``pairwise_poisson`` method is adapted from the ``pairwise bernouilli`` method.
+
+
+See more information:
+
+* :ref:`connection_management`
+
+New parameter for compartmental model
+-------------------------------------
+
+In the compartmental model ``cm_default``, the voltage initialisation of each compartment
+can now be specified by the user, by adding a `v_comp` entry to the compartment parameter dictionary.
+
+See the model documentation:
+
+* :doc:`/models/cm_default`
+
+New interface for NEST Extension Modules
+----------------------------------------
+
+The interface for NEST Extension Modules has been thoroughly revised. Key changes are
+
+* All extention modules must be derived from class ``nest::NESTExtensionInterface`` provided in ``nest_extension_interface.h``.
+
+    * In your class, you must override the ``initialize()`` method with one that registers all components provided by your module.
+    * The ``nest_extension_interface.h`` header provides a large set of NEST includes, so once including it you should no longer need to include other NEST headers in most cases.
+
+* Modules are now unloaded upon ``ResetKernel()`` and new versions of modules can be loaded after ``ResetKernel()``.
+* Modules can now also register connection builders and growth curves with the kernel.
+
+For more information, see the extension module documentation:
+
+* :doc:`NEST Extension Module Documentation <extmod:index>`
+
+NEST requires C++17
+-------------------
+
+From NEST 3.7 on, we use some C++17 features in NEST code. Therefore,
+NEST needs to be built with a compiler that supports C++17. Most
+recent C++ compilers should do so.