diff --git a/doc/htmldoc/faqs/qa-precise-spike-times.rst b/doc/htmldoc/faqs/qa-precise-spike-times.rst
index 6a4b0ae641..d01caed9e0 100644
--- a/doc/htmldoc/faqs/qa-precise-spike-times.rst
+++ b/doc/htmldoc/faqs/qa-precise-spike-times.rst
@@ -140,7 +140,7 @@ Questions and answers about precise neurons
 13. Q: **What is the rate at which spikes are missed in a typical
     large-scale neuronal network simulation of integrate-and-fire model
     neurons with linear subthreshold dynamics in the balanced state and
-    a spike rate of around 10 Hz**?
+    a spike rate of around 10 spks/s**?
 
     A: At a typical parameter setting for a simulation with around 10,000
     neurons and 15 million synapses, the total rate at which spikes are
diff --git a/doc/htmldoc/ref_material/glossary.rst b/doc/htmldoc/ref_material/glossary.rst
index e60c782dc3..12ff3381aa 100644
--- a/doc/htmldoc/ref_material/glossary.rst
+++ b/doc/htmldoc/ref_material/glossary.rst
@@ -41,22 +41,19 @@ Units of measure
    Nanosiemens (nS).
 
  g_L
-   Leak conductance in Nanosiemens (nS).
+   Leak conductance in nanosiemens (nS).
 
  g_K
-   Potassium peak conductance in Nanosiemens (nS).
+   Potassium peak conductance in nanosiemens (nS).
 
  g_Na
-   Sodium peak conductance in Nanosiemens (nS).
+   Sodium peak conductance in nanosiemens (nS).
 
  spike rates
-   Spikes/second.
-
- modulation frequencies
-   Herz (Hz).
+   The number of spikes that occurred in a certain time interval, usually expressed in terms of spikes per second (spks/s or s^-1).
 
  frequency
-   Frequncy in Hertz (Hz).
+   Frequency in Hertz (Hz). Note that spike rates are often better expressed in terms of spikes per second (spks/s or s^-1).
 
  voltage
    Millivolts (mV).
diff --git a/models/mip_generator.cpp b/models/mip_generator.cpp
index 7ff9f6e48d..bf60516e97 100644
--- a/models/mip_generator.cpp
+++ b/models/mip_generator.cpp
@@ -44,7 +44,7 @@ nest::register_mip_generator( const std::string& name )
  * ---------------------------------------------------------------- */
 
 nest::mip_generator::Parameters_::Parameters_()
-  : rate_( 0.0 ) // Hz
+  : rate_( 0.0 ) // spks/s
   , p_copy_( 1.0 )
 {
 }
@@ -114,7 +114,7 @@ nest::mip_generator::pre_run_hook()
 {
   StimulationDevice::pre_run_hook();
 
-  // rate_ is in Hz, dt in ms, so we have to convert from s to ms
+  // rate_ is in spks/s, dt in ms, so we have to convert from s to ms
   poisson_distribution::param_type param( Time::get_resolution().get_ms() * P_.rate_ * 1e-3 );
   V_.poisson_dist_.param( param );
 }
diff --git a/models/mip_generator.h b/models/mip_generator.h
index fc85e151a7..ff1eb91ded 100644
--- a/models/mip_generator.h
+++ b/models/mip_generator.h
@@ -151,7 +151,7 @@ class mip_generator : public StimulationDevice
    */
   struct Parameters_
   {
-    double rate_;   //!< process rate in Hz
+    double rate_;   //!< process rate in spks/s
     double p_copy_; //!< copy probability for each spike in the parent process
 
     Parameters_(); //!< Sets default parameter values
diff --git a/models/poisson_generator.cpp b/models/poisson_generator.cpp
index 82296b00c4..516a886bca 100644
--- a/models/poisson_generator.cpp
+++ b/models/poisson_generator.cpp
@@ -48,7 +48,7 @@ nest::register_poisson_generator( const std::string& name )
  * ---------------------------------------------------------------- */
 
 nest::poisson_generator::Parameters_::Parameters_()
-  : rate_( 0.0 ) // Hz
+  : rate_( 0.0 ) // spks/s
 {
 }
 
@@ -112,7 +112,7 @@ nest::poisson_generator::pre_run_hook()
 {
   StimulationDevice::pre_run_hook();
 
-  // rate_ is in Hz, dt in ms, so we have to convert from s to ms
+  // rate_ is in spks/s, dt in ms, so we have to convert from s to ms
   poisson_distribution::param_type param( Time::get_resolution().get_ms() * P_.rate_ * 1e-3 );
   V_.poisson_dist_.param( param );
 }
diff --git a/models/poisson_generator_ps.cpp b/models/poisson_generator_ps.cpp
index 8f587de1a3..7a913d15bc 100644
--- a/models/poisson_generator_ps.cpp
+++ b/models/poisson_generator_ps.cpp
@@ -51,7 +51,7 @@ nest::register_poisson_generator_ps( const std::string& name )
  * ---------------------------------------------------------------- */
 
 nest::poisson_generator_ps::Parameters_::Parameters_()
-  : rate_( 0.0 )      // Hz
+  : rate_( 0.0 )      // spks/s
   , dead_time_( 0.0 ) // ms
   , num_targets_( 0 )
 {
diff --git a/models/poisson_generator_ps.h b/models/poisson_generator_ps.h
index 075c16a23b..4718c4f055 100644
--- a/models/poisson_generator_ps.h
+++ b/models/poisson_generator_ps.h
@@ -148,7 +148,7 @@ class poisson_generator_ps : public StimulationDevice
    */
   struct Parameters_
   {
-    double rate_;      //!< process rate [Hz]
+    double rate_;      //!< process rate [spks/s]
     double dead_time_; //!< dead time [ms]
 
     /**
diff --git a/models/sinusoidal_poisson_generator.cpp b/models/sinusoidal_poisson_generator.cpp
index 5c8560deaf..6a24cfae5f 100644
--- a/models/sinusoidal_poisson_generator.cpp
+++ b/models/sinusoidal_poisson_generator.cpp
@@ -281,7 +281,7 @@ nest::sinusoidal_poisson_generator::update( Time const& origin, const long from,
         kernel().event_delivery_manager.send( *this, se, lag );
       }
     }
-    // store rate in Hz
+    // store rate in spks/s
     B_.logger_.record_data( origin.get_steps() + lag );
   }
 }
diff --git a/models/step_rate_generator.h b/models/step_rate_generator.h
index c3f3219319..4853c15f7f 100644
--- a/models/step_rate_generator.h
+++ b/models/step_rate_generator.h
@@ -53,7 +53,7 @@ The ``rate_generator`` provides a piecewise constant rate input to the
 connected rate unit(s). Please note that this input is handled in the same
 way as input from any other rate unit, that is, it is processed by the input
 function of the receiving rate unit. The amplitude of the rate is changed
-at the specified times. The unit of the rate is Hz.
+at the specified times. The unit of the rate is spks/s.
 
 If ``allow_offgrid_times`` is false, times will be rounded to the nearest
 grid point if they are less than tic/2 from the grid point, otherwise
diff --git a/nestkernel/growth_curve.h b/nestkernel/growth_curve.h
index 1d4b03b148..9671ea0023 100644
--- a/nestkernel/growth_curve.h
+++ b/nestkernel/growth_curve.h
@@ -104,7 +104,7 @@ class GrowthCurve
  *                          rate that the neuron should achieve.  For example, an
  *                          eps = 0.05 [Ca2+] with tau_Ca = 10000.0 and beta_Ca =
  *                          0.001 for a synaptic element means a desired firing
- *                          rate of 5Hz.
+ *                          rate of 5 spks/s.
  *
  *  References:
  *   [1] Butz, Markus, Florentin Wörgötter, and Arjen van Ooyen.
@@ -184,7 +184,7 @@ class GrowthCurveLinear : public GrowthCurve
  *                          firing rate that the neuron should achieve.  For
  *                          example, an eps = 0.05 [Ca2+] with tau_Ca = 10000.0
  *                          and beta_Ca = 0.001 for a synaptic element means a
- *                          desired firing rate of 5Hz.
+ *                          desired firing rate of 5 spks/s.
  *
  *   nu           double -  Growth rate in elements/ms. The growth rate nu is
  *                          defined in the SynapticElement class. Can be negative.
@@ -265,7 +265,7 @@ class GrowthCurveGaussian : public GrowthCurve
  *                          firing rate that the neuron should achieve.  For
  *                          example, an eps = 0.05 [Ca2+] with tau_Ca = 10000.0
  *                          and beta_Ca = 0.001 for a synaptic element means a
- *                          desired firing rate of 5Hz.
+ *                          desired firing rate of 5 spks/s.
  *
  *   nu           double -  Growth rate in elements/ms. The growth rate nu is
  *                          defined in the SynapticElement class. Can be negative.
diff --git a/nestkernel/structural_plasticity_node.h b/nestkernel/structural_plasticity_node.h
index 55c0847488..ffaf0c9f67 100644
--- a/nestkernel/structural_plasticity_node.h
+++ b/nestkernel/structural_plasticity_node.h
@@ -130,7 +130,7 @@ class StructuralPlasticityNode : public Node
    *
    * Intracellular calcium concentration has a linear factor to mean
    * electrical activity of 10^2, this means, for example, that a [Ca2+] of
-   * 0.2 is equivalent to a mean activity of 20 Hz.
+   * 0.2 is equivalent to a mean activity of 20 spks/s.
    */
   double Ca_minus_;
 
diff --git a/pynest/examples/astrocytes/astrocyte_interaction.py b/pynest/examples/astrocytes/astrocyte_interaction.py
index f75fa0ea16..7634ef4be9 100644
--- a/pynest/examples/astrocytes/astrocyte_interaction.py
+++ b/pynest/examples/astrocytes/astrocyte_interaction.py
@@ -134,7 +134,7 @@
 axes[1].plot(data_astro["times"], data_astro["IP3"])
 axes[2].plot(data_post["times"], data_post["I_SIC"])
 axes[3].plot(data_astro["times"], data_astro["Ca_astro"])
-axes[0].set_title(f"Presynaptic neuron\n(Poisson rate = {poisson_rate_neuro} Hz)")
+axes[0].set_title(f"Presynaptic neuron\n(Poisson rate = {poisson_rate_neuro} spks/s)")
 axes[0].set_ylabel("Membrane potential (mV)")
 axes[2].set_title("Postsynaptic neuron")
 axes[2].set_ylabel("Slow inward current (pA)")
diff --git a/pynest/examples/balancedneuron.py b/pynest/examples/balancedneuron.py
index d815a07c06..a76cecc587 100644
--- a/pynest/examples/balancedneuron.py
+++ b/pynest/examples/balancedneuron.py
@@ -123,13 +123,13 @@
 
 
 def output_rate(guess):
-    print("Inhibitory rate estimate: %5.2f Hz" % guess)
+    print(f"Inhibitory rate estimate: {guess:5.2f} spks/s")
     rate = float(abs(n_in * guess))
     noise[1].rate = rate
     spikerecorder.n_events = 0
     nest.Simulate(t_sim)
     out = spikerecorder.n_events * 1000.0 / t_sim
-    print("  -> Neuron rate: %6.2f Hz (goal: %4.2f Hz)" % (out, r_ex))
+    print(f"  -> Neuron rate: {out:6.2f} spks/s (goal: {r_ex:4.2f} spks/s)")
     return out
 
 
@@ -143,13 +143,13 @@ def output_rate(guess):
 # During simulation, the ``spike_recorder`` counts the spikes of the target
 # neuron and the total number is read out at the end of the simulation
 # period. The return value of ``output_rate()`` is the firing rate of the
-# target neuron in Hz.
+# target neuron in spks/s.
 #
 # Second, the scipy function ``bisect`` is used to determine the optimal
 # firing rate of the neurons of the inhibitory population.
 
 in_rate = bisect(lambda x: output_rate(x) - r_ex, lower, upper, xtol=prec)
-print("Optimal rate for the inhibitory population: %.2f Hz" % in_rate)
+print(f"Optimal rate for the inhibitory population: {in_rate:.2f} spks/s")
 
 ###############################################################################
 # The function ``bisect`` takes four arguments: first a function whose
diff --git a/pynest/examples/brunel_alpha_nest.py b/pynest/examples/brunel_alpha_nest.py
index cae0a6ca1e..5c2217d139 100755
--- a/pynest/examples/brunel_alpha_nest.py
+++ b/pynest/examples/brunel_alpha_nest.py
@@ -154,7 +154,7 @@ def ComputePSPnorm(tauMem, CMem, tauSyn):
 # Definition of threshold rate, which is the external rate needed to fix the
 # membrane potential around its threshold, the external firing rate and the
 # rate of the poisson generator which is multiplied by the in-degree CE and
-# converted to Hz by multiplication by 1000.
+# converted to spks/s by multiplication by 1000.
 
 nu_th = (theta * CMem) / (J_ex * CE * np.exp(1) * tauMem * tauSyn)
 nu_ex = eta * nu_th
@@ -281,7 +281,7 @@ def ComputePSPnorm(tauMem, CMem, tauSyn):
 # Calculation of the average firing rate of the excitatory and the inhibitory
 # neurons by dividing the total number of recorded spikes by the number of
 # neurons recorded from and the simulation time. The multiplication by 1000.0
-# converts the unit 1/ms to 1/s=Hz.
+# converts the unit 1/ms to 1/s.
 
 rate_ex = events_ex / simtime * 1000.0 / N_rec
 rate_in = events_in / simtime * 1000.0 / N_rec
@@ -310,8 +310,8 @@ def ComputePSPnorm(tauMem, CMem, tauSyn):
 print(f"Number of synapses: {num_synapses}")
 print(f"       Excitatory : {num_synapses_ex}")
 print(f"       Inhibitory : {num_synapses_in}")
-print(f"Excitatory rate   : {rate_ex:.2f} Hz")
-print(f"Inhibitory rate   : {rate_in:.2f} Hz")
+print(f"Excitatory rate   : {rate_ex:.2f} spks/s")
+print(f"Inhibitory rate   : {rate_in:.2f} spks/s")
 print(f"Building time     : {build_time:.2f} s")
 print(f"Simulation time   : {sim_time:.2f} s")
 
diff --git a/pynest/examples/brunel_delta_nest.py b/pynest/examples/brunel_delta_nest.py
index 6d8d27bd69..863a15f79a 100755
--- a/pynest/examples/brunel_delta_nest.py
+++ b/pynest/examples/brunel_delta_nest.py
@@ -106,7 +106,7 @@
 # Definition of threshold rate, which is the external rate needed to fix the
 # membrane potential around its threshold, the external firing rate and the
 # rate of the poisson generator which is multiplied by the in-degree CE and
-# converted to Hz by multiplication by 1000.
+# converted to spks/s by multiplication by 1000.
 
 nu_th = theta / (J * CE * tauMem)
 nu_ex = eta * nu_th
@@ -233,7 +233,7 @@
 # Calculation of the average firing rate of the excitatory and the inhibitory
 # neurons by dividing the total number of recorded spikes by the number of
 # neurons recorded from and the simulation time. The multiplication by 1000.0
-# converts the unit 1/ms to 1/s=Hz.
+# converts the unit 1/ms to 1/s.
 
 rate_ex = events_ex / simtime * 1000.0 / N_rec
 rate_in = events_in / simtime * 1000.0 / N_rec
@@ -262,8 +262,8 @@
 print(f"Number of synapses: {num_synapses}")
 print(f"       Excitatory : {num_synapses_ex}")
 print(f"       Inhibitory : {num_synapses_in}")
-print(f"Excitatory rate   : {rate_ex:.2f} Hz")
-print(f"Inhibitory rate   : {rate_in:.2f} Hz")
+print(f"Excitatory rate   : {rate_ex:.2f} spks/s")
+print(f"Inhibitory rate   : {rate_in:.2f} spks/s")
 print(f"Building time     : {build_time:.2f} s")
 print(f"Simulation time   : {sim_time:.2f} s")
 
diff --git a/pynest/examples/brunel_exp_multisynapse_nest.py b/pynest/examples/brunel_exp_multisynapse_nest.py
index bcd5eec8ca..815ca783f5 100644
--- a/pynest/examples/brunel_exp_multisynapse_nest.py
+++ b/pynest/examples/brunel_exp_multisynapse_nest.py
@@ -130,7 +130,7 @@
 # Definition of threshold rate, which is the external rate needed to fix the
 # membrane potential around its threshold, the external firing rate and the
 # rate of the poisson generator which is multiplied by the in-degree CE and
-# converted to Hz by multiplication by 1000.
+# converted to spks/s by multiplication by 1000.
 
 nu_th = theta / (J * CE * tauMem)
 nu_ex = eta * nu_th
@@ -265,7 +265,7 @@
 # Calculation of the average firing rate of the excitatory and the inhibitory
 # neurons by dividing the total number of recorded spikes by the number of
 # neurons recorded from and the simulation time. The multiplication by 1000.0
-# converts the unit 1/ms to 1/s=Hz.
+# converts the unit 1/ms to 1/s.
 
 rate_ex = events_ex / simtime * 1000.0 / N_rec
 rate_in = events_in / simtime * 1000.0 / N_rec
@@ -294,8 +294,8 @@
 print(f"Number of synapses: {num_synapses}")
 print(f"       Excitatory : {num_synapses_ex}")
 print(f"       Inhibitory : {num_synapses_in}")
-print(f"Excitatory rate   : {rate_ex:.2f} Hz")
-print(f"Inhibitory rate   : {rate_in:.2f} Hz")
+print(f"Excitatory rate   : {rate_ex:.2f} spks/s")
+print(f"Inhibitory rate   : {rate_in:.2f} spks/s")
 print(f"Building time     : {build_time:.2f} s")
 print(f"Simulation time   : {sim_time:.2f} s")
 
diff --git a/pynest/examples/brunel_siegert_nest.py b/pynest/examples/brunel_siegert_nest.py
index eb82703fb1..f6f0a22dc4 100644
--- a/pynest/examples/brunel_siegert_nest.py
+++ b/pynest/examples/brunel_siegert_nest.py
@@ -202,5 +202,5 @@
 rates_ex = data["rate"][numpy.where(data["senders"] == siegert_ex.global_id)]
 rates_in = data["rate"][numpy.where(data["senders"] == siegert_in.global_id)]
 times = data["times"][numpy.where(data["senders"] == siegert_in.global_id)]
-print(f"Excitatory rate   : {rates_ex[-1]:.2f} Hz")
-print(f"Inhibitory rate   : {rates_in[-1]:.2f} Hz")
+print(f"Excitatory rate   : {rates_ex[-1]:.2f} spks/s")
+print(f"Inhibitory rate   : {rates_in[-1]:.2f} spks/s")
diff --git a/pynest/examples/gap_junctions_inhibitory_network.py b/pynest/examples/gap_junctions_inhibitory_network.py
index 18a0d0d9a0..8c68754f31 100644
--- a/pynest/examples/gap_junctions_inhibitory_network.py
+++ b/pynest/examples/gap_junctions_inhibitory_network.py
@@ -143,11 +143,11 @@
 spikes = events["senders"]
 n_spikes = sr.n_events
 
-hz_rate = (1000.0 * n_spikes / simtime) / n_neuron
+spike_rate = (1000.0 * n_spikes / simtime) / n_neuron
 
 plt.figure(1)
 plt.plot(times, spikes, "o")
-plt.title(f"Average spike rate (Hz): {hz_rate:.2f}")
+plt.title(f"Average spike rate: {spike_rate:.2f} spks/s")
 plt.xlabel("time (ms)")
 plt.ylabel("neuron no")
 plt.show()
diff --git a/pynest/examples/gif_pop_psc_exp.py b/pynest/examples/gif_pop_psc_exp.py
index 6867ff4206..f1b091bac2 100644
--- a/pynest/examples/gif_pop_psc_exp.py
+++ b/pynest/examples/gif_pop_psc_exp.py
@@ -226,12 +226,12 @@
 plt.figure(1)
 plt.clf()
 plt.subplot(2, 1, 1)
-plt.plot(t, A_N * 1000)  # plot population activities (in Hz)
-plt.ylabel(r"$A_N$ [Hz]")
+plt.plot(t, A_N * 1000)  # plot population activities (in spks/s)
+plt.ylabel(r"$A_N$ [spks/s]")
 plt.title("Population activities (mesoscopic sim.)")
 plt.subplot(2, 1, 2)
 plt.plot(t, Abar * 1000)  # plot instantaneous population rates (in Hz)
-plt.ylabel(r"$\bar A$ [Hz]")
+plt.ylabel(r"$\bar A$ [spks/s]")
 plt.xlabel("time [ms]")
 
 ###############################################################################
@@ -334,19 +334,19 @@
 
 ###############################################################################
 # Let's retrieve the data of the spike recorder and plot the activity of the
-# excitatory population (in Hz):
+# excitatory population (in spks/s):
 
 for i in range(len(nest_pops)):
     data_sr = nest_sr[i].get("events", "times") * dt - t0
     bins = np.concatenate((t, np.array([t[-1] + dt_rec])))
     A = np.histogram(data_sr, bins=bins)[0] / float(N[i]) / dt_rec
-    A_N[:, i] = A * 1000  # in Hz
+    A_N[:, i] = A * 1000  # in spks/s
 
 t = np.arange(dt, t_end + dt, dt_rec)
 plt.figure(2)
 plt.plot(t, A_N[:, 0])
 plt.xlabel("time [ms]")
-plt.ylabel("population activity [Hz]")
+plt.ylabel("population activity [spks/s]")
 plt.title("Population activities (microscopic sim.)")
 
 ###############################################################################
diff --git a/pynest/examples/gif_population.py b/pynest/examples/gif_population.py
index 9f73dbccdb..bd005e710a 100644
--- a/pynest/examples/gif_population.py
+++ b/pynest/examples/gif_population.py
@@ -95,7 +95,7 @@
 # GIF neurons population.
 
 N_noise = 50  # size of Poisson group
-rate_noise = 10.0  # firing rate of Poisson neurons (Hz)
+rate_noise = 10.0  # firing rate of Poisson neurons (spks/s)
 w_noise = 20.0  # synaptic weights from Poisson to population neurons (pA)
 
 ###############################################################################
diff --git a/pynest/examples/one_neuron_with_noise.py b/pynest/examples/one_neuron_with_noise.py
index ea64005bd2..44e5954611 100755
--- a/pynest/examples/one_neuron_with_noise.py
+++ b/pynest/examples/one_neuron_with_noise.py
@@ -56,9 +56,9 @@
 ###############################################################################
 # Third, the Poisson generator is configured using ``SetStatus``, which expects
 # a list of node handles and a list of parameter dictionaries. We set the
-# Poisson generators to 80,000 Hz and 15,000 Hz, respectively. Note that we do
-# not need to set parameters for the neuron and the voltmeter, since they have
-# satisfactory defaults.
+# Poisson generators to 80,000 spks/s and 15,000 spks/s, respectively. Note that
+# we do not need to set parameters for the neuron and the voltmeter, since they
+# have satisfactory defaults.
 
 noise[0].rate = 80000.0
 noise[1].rate = 15000.0
diff --git a/pynest/examples/pong/networks.py b/pynest/examples/pong/networks.py
index 4081888d6f..db5aa188b9 100644
--- a/pynest/examples/pong/networks.py
+++ b/pynest/examples/pong/networks.py
@@ -259,7 +259,7 @@ class PongNetDopa(PongNet):
     # Synaptic delay for the direct connection between striatum and
     # dopaminergic neurons
     d_dir = 200
-    # Rate (Hz) for the background poisson generators
+    # Rate (spks/s) for the background poisson generators
     poisson_rate = 15
 
     def __init__(self, apply_noise=True, num_neurons=20):
diff --git a/pynest/examples/sudoku/sudoku_net.py b/pynest/examples/sudoku/sudoku_net.py
index b709010fae..06bb28f05e 100644
--- a/pynest/examples/sudoku/sudoku_net.py
+++ b/pynest/examples/sudoku/sudoku_net.py
@@ -232,6 +232,6 @@ def set_noise_rate(self, rate):
         Parameters
         ----------
         rate : float
-            average spike frequency in Hz
+            average spike rate in spks/s
         """
         self.noise.rate = rate
diff --git a/pynest/examples/urbanczik_synapse_example.py b/pynest/examples/urbanczik_synapse_example.py
index be5ec7f791..8c09e03e2b 100644
--- a/pynest/examples/urbanczik_synapse_example.py
+++ b/pynest/examples/urbanczik_synapse_example.py
@@ -217,7 +217,7 @@ def h(U, nrn_params):
 # poisson generators. The recorded spike times are then given to spike
 # generators.
 n_pg = 200  # number of poisson generators
-p_rate = 10.0  # rate in Hz
+p_rate = 10.0  # rate in spks/s
 
 pgs = nest.Create("poisson_generator", n=n_pg, params={"rate": p_rate})
 
diff --git a/pynest/nest/raster_plot.py b/pynest/nest/raster_plot.py
index fa6450762c..410125c51f 100644
--- a/pynest/nest/raster_plot.py
+++ b/pynest/nest/raster_plot.py
@@ -268,7 +268,7 @@ def _make_plot(ts, ts1, node_ids, neurons, hist=True, hist_binwidth=5.0, graysca
 
         plt.bar(t_bins, heights, width=hist_binwidth, color=color_bar, edgecolor=color_edge)
         plt.yticks([int(x) for x in numpy.linspace(0.0, int(max(heights) * 1.1) + 5, 4)])
-        plt.ylabel("Rate (Hz)")
+        plt.ylabel("Rate (spks/s)")
         plt.xlabel(xlabel)
         plt.xlim(xlim)
         plt.axes(ax1)
diff --git a/testsuite/pytests/test_jonke_synapse.py b/testsuite/pytests/test_jonke_synapse.py
index 2b96fc7871..2fc3fd324d 100644
--- a/testsuite/pytests/test_jonke_synapse.py
+++ b/testsuite/pytests/test_jonke_synapse.py
@@ -38,8 +38,8 @@ class TestJonkeSynapse:
     """
 
     resolution = 0.1  # [ms]
-    presynaptic_firing_rate = 20.0  # [Hz]
-    postsynaptic_firing_rate = 20.0  # [Hz]
+    presynaptic_firing_rate = 20.0  # [spks/s]
+    postsynaptic_firing_rate = 20.0  # [spks/s]
     simulation_duration = 1e4  # [ms]
     hardcoded_trains_length = 15.0  # [ms]
     synapse_parameters = {
diff --git a/testsuite/pytests/test_poisson_generator_campbell_alpha.py b/testsuite/pytests/test_poisson_generator_campbell_alpha.py
index 1c3e427b09..12aca85afa 100644
--- a/testsuite/pytests/test_poisson_generator_campbell_alpha.py
+++ b/testsuite/pytests/test_poisson_generator_campbell_alpha.py
@@ -46,7 +46,7 @@ def test_poisson_generator_alpha():
     tolerance = 0.05
     V_m_target = 20.0
     J = 0.01
-    # solve for rate, convert to Hz, drop C_m as it is 1.
+    # solve for rate, convert to spks/s, drop C_m as it is 1.
     rate = 1000.0 * V_m_target / (J * neuron_params["tau_m"] * np.exp(1) * neuron_params["tau_syn_ex"])
 
     poisson = nest.Create("poisson_generator")
diff --git a/testsuite/pytests/test_stdp_nn_synapses.py b/testsuite/pytests/test_stdp_nn_synapses.py
index ae25882ff5..7707b525d9 100644
--- a/testsuite/pytests/test_stdp_nn_synapses.py
+++ b/testsuite/pytests/test_stdp_nn_synapses.py
@@ -48,8 +48,8 @@ class TestSTDPNNSynapses:
     @pytest.fixture(autouse=True)
     def setUp(self):
         self.resolution = 0.1  # [ms]
-        self.presynaptic_firing_rate = 20.0  # [Hz]
-        self.postsynaptic_firing_rate = 20.0  # [Hz]
+        self.presynaptic_firing_rate = 20.0  # [spks/s]
+        self.postsynaptic_firing_rate = 20.0  # [spks/s]
         self.simulation_duration = 1e4  # [ms]
         self.synapse_parameters = {
             "receptor_type": 1,
diff --git a/testsuite/pytests/test_tsodyks2_synapse.py b/testsuite/pytests/test_tsodyks2_synapse.py
index f5746c7041..fdc8803586 100644
--- a/testsuite/pytests/test_tsodyks2_synapse.py
+++ b/testsuite/pytests/test_tsodyks2_synapse.py
@@ -35,7 +35,7 @@ class Tsodyks2SynapseTest(unittest.TestCase):
 
     def setUp(self):
         self.resolution = 0.1  # [ms]
-        self.presynaptic_firing_rate = 20.0  # [Hz]
+        self.presynaptic_firing_rate = 20.0  # [spks/s]
         self.simulation_duration = 1e3  # [ms]
         self.hardcoded_trains_length = 15.0  # [ms]
         self.synapse_parameters = {