diff --git a/pynest/examples/eprop_plasticity/eprop_supervised_classification_evidence-accumulation_bsshslm_2020.py b/pynest/examples/eprop_plasticity/eprop_supervised_classification_evidence-accumulation_bsshslm_2020.py
index 76cd266b7c..f95953250a 100644
--- a/pynest/examples/eprop_plasticity/eprop_supervised_classification_evidence-accumulation_bsshslm_2020.py
+++ b/pynest/examples/eprop_plasticity/eprop_supervised_classification_evidence-accumulation_bsshslm_2020.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_evidence-accumulation_bsshslm_2020.png
+.. image:: eprop_supervised_classification_evidence-accumulation_bsshslm_2020.png
    :width: 70 %
    :alt: Schematic of network architecture. Same as Figure 1 in the code.
    :align: center
diff --git a/pynest/examples/eprop_plasticity/eprop_supervised_classification_neuromorphic_mnist.py b/pynest/examples/eprop_plasticity/eprop_supervised_classification_neuromorphic_mnist.py
index d29e709399..d408f73739 100644
--- a/pynest/examples/eprop_plasticity/eprop_supervised_classification_neuromorphic_mnist.py
+++ b/pynest/examples/eprop_plasticity/eprop_supervised_classification_neuromorphic_mnist.py
@@ -37,7 +37,7 @@
 binary events, which we interpret as spike trains. This conversion closely emulates biological neural
 processing, making it a fitting challenge for an e-prop-equipped spiking neural network (SNN).
 
-.. image:: ../../../../pynest/examples/eprop_plasticity/eprop_supervised_classification_evidence-accumulation.png
+.. image:: eprop_supervised_classification_evidence-accumulation.png
    :width: 70 %
    :alt: Schematic of network architecture. Same as Figure 1 in the code.
    :align: center
diff --git a/pynest/examples/eprop_plasticity/eprop_supervised_regression_sine-waves_bsshslm_2020.py b/pynest/examples/eprop_plasticity/eprop_supervised_regression_sine-waves_bsshslm_2020.py
index 5b523a2bfb..50a8248dc8 100644
--- a/pynest/examples/eprop_plasticity/eprop_supervised_regression_sine-waves_bsshslm_2020.py
+++ b/pynest/examples/eprop_plasticity/eprop_supervised_regression_sine-waves_bsshslm_2020.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_sine-waves_bsshslm_2020.png
+.. image:: eprop_supervised_regression_sine-waves_bsshslm_2020.png
    :width: 70 %
    :alt: Schematic of network architecture. Same as Figure 1 in the code.
    :align: center