diff --git a/doc/htmldoc/benchmark_results.rst b/doc/htmldoc/benchmark_results.rst
index a36685e9e9..bc0bf0fecc 100644
--- a/doc/htmldoc/benchmark_results.rst
+++ b/doc/htmldoc/benchmark_results.rst
@@ -5,10 +5,10 @@ NEST performance benchmarks
 
 
 NEST performance is continuously monitored and improved across various network sizes.
-Here we show benchmarking results for NEST version 3.8 on Jureca-DC.
+Here we show benchmarking results for NEST version 3.8 on Jureca-DC [1]_.
+The benchmarking framework and the structure of the graphs is described in [2]_.
 
-
-Strong scaling experiment of the Microcircuit model [1]_
+Strong scaling experiment of the Microcircuit model [3]_
 ---------------------------------------------------------
 
 .. grid:: 1 1 1 1
@@ -26,14 +26,16 @@ Strong scaling experiment of the Microcircuit model [1]_
        :class: sd-align-minor-center
 
 
-       * The model has ~80 000 neurons and ~300 million synapses
+       * The model has ~80 000 neurons and ~300 million synapses, minimal delay 0.1 ms
+       * 2 MPI processes per node, 64 threads per MPI process
        * Increasing number of computing resources decrease simulation time
-       * The model runs faster than real time
+       * Data averaged over 3 runs with different seeds, error bars indicate standard deviation
+       * The model runs faster than real time [4]_
 
 
 
 
-Strong scaling experiment of the Multi-area-model [2]_
+Strong scaling experiment of the Multi-area-model [5]_
 -------------------------------------------------------
 
 .. grid:: 1 1 1 1
@@ -51,13 +53,15 @@ Strong scaling experiment of the Multi-area-model [2]_
        :columns: 10
        :class: sd-align-minor-center
 
-       * The model has ~4.1 million neurons and ~24 billion synapses
+       * The model has ~4.1 million neurons and ~24 billion synapses, minimal delay 0.1 ms
+       * 2 MPI processes per node, 64 threads per MPI process
        * Steady decrease of run time with additional compute resources
+       * Data averaged over 3 runs with different seeds, error bars indicate standard deviation
 
 
 
 
-Weak scaling experiment of the HPC benchmark model [3]_
+Weak scaling experiment of the HPC benchmark model [6]_
 --------------------------------------------------------
 
 .. grid:: 1 1 1 1
@@ -77,8 +81,10 @@ Weak scaling experiment of the HPC benchmark model [3]_
 
 
        * The size of network scales proportionally with the computational resources used
-       * Largest network size in this diagram: ~5.8 million neurons and ~65 billion synapses
+       * Largest network size in this diagram: ~5.8 million neurons and ~65 billion synapses, minimal delay 1.5 ms
+       * 2 MPI processes per node, 64 threads per MPI process
        * The figure shows that NEST can handle massive networks and simulate them efficiently
+       * Data averaged over 3 runs with different seeds, error bars indicate standard deviation
 
 
 .. seealso::
@@ -92,15 +98,27 @@ Weak scaling experiment of the HPC benchmark model [3]_
 References
 ----------
 
-.. [1]  Potjans TC. and Diesmann M. 2014. The cell-type specific cortical
+.. [1]  Juelich Supercomputing Centre. 2021.  JURECA: Data Centric and Booster Modules implementing the Modular
+        Supercomputing Architecture at Jülich Supercomputing Centre. Journal of large-scale research facilities,
+        7, A182. DOI: http://dx.doi.org/10.17815/jlsrf-7-182
+
+
+.. [2]  Albers J, Pronold J, Kurth AC, Vennemo SB, Haghighi Mood K, Patronis A, Terhorst D, Jordan J, Kunkel S,
+        Tetzlaff T, Diesmann M and Senk J (2022). A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.
+        Frontiers in Neuroinformatics(16):837549. https://doi.org/10.3389/fninf.2022.837549
+
+.. [3]  Potjans TC. and Diesmann M. 2014. The cell-type specific cortical
         microcircuit: relating structure and activity in a full-scale spiking
         network model. Cerebral Cortex. 24(3):785–806. DOI: `10.1093/cercor/bhs358 <https://doi.org/10.1093/cercor/bhs358>`__.
 
+.. [4]  Kurth AC, Senk J, Terhorst D, Finnerty J, Diesmann M. 2022. Sub-realtime simulation of a neuronal network of natural density.
+        Neuromorphic computing and engineering 2(2), 021001
+        https://iopscience.iop.org/article/10.1088/2634-4386/ac55fc/meta
 
-.. [2] Schmidt M, Bakker R, Hilgetag CC, Diesmann M and van Albada SJ. 2018. Multi-scale
-       account of the network structure of macaque visual cortex. Brain Structure
-       and Function. 223: 1409 https://doi.org/10.1007/s00429-017-1554-4
+.. [5]  Schmidt M, Bakker R, Hilgetag CC, Diesmann M and van Albada SJ. 2018. Multi-scale
+        account of the network structure of macaque visual cortex. Brain Structure
+        and Function. 223: 1409 https://doi.org/10.1007/s00429-017-1554-4
 
-.. [3] Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S. 2018.
-       Extremely scalable spiking neuronal network simulation code: From laptops to exacale computers.
-       Frontiers in Neuroinformatics. 12. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00002
+.. [6]  Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S. 2018.
+        Extremely scalable spiking neuronal network simulation code: From laptops to exacale computers.
+        Frontiers in Neuroinformatics. 12. https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00002