Update eprop example images

Author: JesusEV

pynest/examples/eprop_plasticity/README.rst

@@ -2,7 +2,7 @@ E-prop plasticity examples
 ==========================
 
 
-.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_regression_infrastructure.png
+.. image:: ../../../../pynest/examples/eprop_plasticity/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

pynest/examples/eprop_plasticity/eprop_supervised_classification_evidence-accumulation.py

@@ -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_infrastructure.png
+.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_classification_schematic_evidence-accumulation.png
    :width: 70 %
    :alt: See Figure 1 below.
    :align: center
@@ -92,7 +92,7 @@
 # synapse models below. The connections that must be established are numbered 1 to 7.
 
 try:
-    Image(filename="./eprop_supervised_classification_infrastructure.png")
+    Image(filename="./eprop_supervised_classification_schematic_evidence-accumulation.png")
 except Exception:
     pass
 

pynest/examples/eprop_plasticity/eprop_supervised_classification_infrastructure.png renamed to pynest/examples/eprop_plasticity/eprop_supervised_classification_schematic_evidence-accumulation.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_infrastructure.png
+.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_regression_schematic_handwriting.png
    :width: 70 %
    :alt: See Figure 1 below.
    :align: center
@@ -90,7 +90,7 @@
 # synapse models below. The connections that must be established are numbered 1 to 6.
 
 try:
-    Image(filename="./eprop_supervised_regression_infrastructure.png")
+    Image(filename="./eprop_supervised_regression_schematic_handwriting.png")
 except Exception:
     pass
 

pynest/examples/eprop_plasticity/eprop_supervised_regression_handwriting.py

@@ -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_infrastructure.png
+.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_regression_schematic_infinite-loop.png
    :width: 70 %
    :alt: See Figure 1 below.
    :align: center
@@ -90,7 +90,7 @@
 # synapse models below. The connections that must be established are numbered 1 to 6.
 
 try:
-    Image(filename="./eprop_supervised_regression_infrastructure.png")
+    Image(filename="./eprop_supervised_regression_schematic_infinite-loop.png")
 except Exception:
     pass
 

pynest/examples/eprop_plasticity/eprop_supervised_regression_infinite-loop.py

@@ -38,7 +38,7 @@
 network learns to reproduce with its overall spiking activity a one-dimensional, one-second-long target signal
 which is a superposition of four sine waves of different amplitudes, phases, and periods.
 
-.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_regression_infrastructure.png
+.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_regression_schematic_sine-waves.png
    :width: 70 %
    :alt: See Figure 1 below.
    :align: center
@@ -86,7 +86,7 @@
 # synapse models below. The connections that must be established are numbered 1 to 6.
 
 try:
-    Image(filename="./eprop_supervised_regression_infrastructure.png")
+    Image(filename="./eprop_supervised_regression_schematic_sine-waves.png")
 except Exception:
     pass