move around items

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
 -------------------------------
@@ -42,28 +56,6 @@ See connectivity documentation:
 * :ref:`tripartite_connectivity`
 
 
-E-prop plasticity in NEST
--------------------------
-
-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
-
-
 Ignore-and-fire neuron model
 ----------------------------
 
@@ -117,9 +109,8 @@ 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``.
-
+* 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.
 
@@ -129,3 +120,10 @@ The interface for NEST Extension Modules has been thoroughly revised. Key change
 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.