move images back to pynest/examples, update links

Author: jessica-mitchell

doc/htmldoc/static/img/pynest/EI_clustered_network_schematic.png renamed to pynest/examples/EI_clustered_network/EI_clustered_network_schematic.png

@@ -3,7 +3,7 @@ EI-clustered circuit model
 
 This is PyNEST implementation of the EI-clustered circuit model described by Rostami et al. [1]_.
 
-.. figure:: /static/img/pynest/EI_clustered_network_schematic.png
+.. figure:: EI_clustered_network_schematic.png
    :alt: EI-clustered circuit model.
 
    Schematic of the EI-clustered circuit model. The network consists of `n_clusters` with one excitatory and one inhibitory population each.

pynest/examples/EI_clustered_network/README.rst

@@ -9,15 +9,15 @@ The network model represents four layers of cortex, L2/3, L4, L5, and L6, each c
 
    .. grid-item::
 
-     .. image:: ../../static/img/potjans_2014_microcircuit.png
+     .. image:: potjans_2014_microcircuit.png
 
    .. grid-item::
 
-     .. image:: ../../static/img/potjans_2014_raster_plot.png
+     .. image:: potjans_2014_raster_plot.png
 
    .. grid-item::
 
-     .. image:: ../../static/img/potjans_2014_box_plot.png
+     .. image:: potjans_2014_box_plot.png
 
 
 

pynest/examples/Potjans_2014/README.rst

@@ -2,7 +2,7 @@ E-prop plasticity examples
 ==========================
 
 
-.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_regression_schematic_sine-waves.png
+.. image:: eprop_supervised_regression_schematic_sine-waves.png
 
 Eligibility propagation (e-prop) [1]_ is a three-factor learning rule for spiking neural networks
 that approximates backpropagation through time. The original TensorFlow implementation of e-prop

doc/htmldoc/static/img/potjans_2014_box_plot.png renamed to pynest/examples/Potjans_2014/potjans_2014_box_plot.png

@@ -40,7 +40,7 @@
 infer the underlying rationale of the task. Here, the solution is to turn to the side in which more cues were
 presented.
 
-.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_classification_schematic_evidence-accumulation.png
+.. image:: eprop_supervised_classification_schematic_evidence-accumulation.png
    :width: 70 %
    :alt: See Figure 1 below.
    :align: center

doc/htmldoc/static/img/potjans_2014_microcircuit.png renamed to pynest/examples/Potjans_2014/potjans_2014_microcircuit.png

@@ -39,7 +39,7 @@
 learns to reproduce with its overall spiking activity a two-dimensional, roughly one-second-long target signal
 which encode the x and y coordinates of the handwritten word "chaos".
 
-.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_regression_schematic_handwriting.png
+.. image:: eprop_supervised_regression_schematic_handwriting.png
    :width: 70 %
    :alt: See Figure 1 below.
    :align: center
@@ -57,6 +57,16 @@
 Charl Linssen, inspired by activities and feedback received at the CapoCaccia Workshop toward Neuromorphic
 Intelligence 2023.
 
+
+Get the chaos_handwriting.txt file:
+
+.. grid::
+
+    .. grid-item-card::
+       :columns: 3
+
+       :download:`chaos_handwriting.txt`
+
 References
 ~~~~~~~~~~
 

doc/htmldoc/static/img/potjans_2014_raster_plot.png renamed to pynest/examples/Potjans_2014/potjans_2014_raster_plot.png

@@ -39,7 +39,7 @@
 learns to reproduce with its overall spiking activity a two-dimensional, roughly two-second-long target signal
 which encode the x and y coordinates of an infinite-loop.
 
-.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_regression_schematic_infinite-loop.png
+.. image:: eprop_supervised_regression_schematic_infinite-loop.png
    :width: 70 %
    :alt: See Figure 1 below.
    :align: center