diff --git a/doc/conf.py b/doc/conf.py
index 6403cd65b..173c86876 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -138,3 +138,7 @@
     'reference_url': {'fooof': None},
     'remove_config_comments': True,
 }
+
+intersphinx_mapping = {
+    'neurodsp': ('https://neurodsp-tools.github.io/neurodsp/', None),
+}
diff --git a/motivations/concepts/plot_DoYouEvenOscillate.py b/motivations/concepts/plot_DoYouEvenOscillate.py
index 9ef27ef17..ce69c1dab 100644
--- a/motivations/concepts/plot_DoYouEvenOscillate.py
+++ b/motivations/concepts/plot_DoYouEvenOscillate.py
@@ -46,14 +46,14 @@
 
 # Use NeuroDSP for time series simulations & analyses
 from neurodsp import sim
-from neurodsp.utils import create_times, set_random_seed
+from neurodsp.utils import create_times
 from neurodsp.spectral import compute_spectrum_welch
 from neurodsp.plts import plot_time_series, plot_power_spectra
 
 ###################################################################################################
 
 # Set random seed, for consistency generating simulated data
-set_random_seed(21)
+sim.set_random_seed(21)
 
 # Simulation Settings
 n_seconds = 2
diff --git a/motivations/concepts/plot_IfYouFilterTheyWillCome.py b/motivations/concepts/plot_IfYouFilterTheyWillCome.py
index 1cd342216..76a8ca8d1 100644
--- a/motivations/concepts/plot_IfYouFilterTheyWillCome.py
+++ b/motivations/concepts/plot_IfYouFilterTheyWillCome.py
@@ -33,10 +33,10 @@
 from fooof.bands import Bands
 
 # Imports from NeuroDSP to simulate & plot time series
-from neurodsp.sim import sim_powerlaw
+from neurodsp.sim import sim_powerlaw, set_random_seed
 from neurodsp.filt import filter_signal
 from neurodsp.plts import plot_time_series
-from neurodsp.utils import create_times, set_random_seed
+from neurodsp.utils import create_times
 
 ###################################################################################################
 
@@ -96,8 +96,8 @@
     band_sig = filter_signal(sig, s_rate, 'bandpass', f_range)
 
     # Plot the time series of the current band, and adjust plot aesthetics
-    plot_time_series(times, band_sig, title=label + ' ' + str(f_range), ax=ax)
-    ax.set_xlim(0, n_seconds); ax.set_ylim(-1, 1); ax.set_xlabel('');
+    plot_time_series(times, band_sig, title=label + ' ' + str(f_range), ax=ax,
+                     xlim=(0, n_seconds), ylim=(-1, 1), xlabel='')
 
 ###################################################################################################
 #
@@ -159,8 +159,7 @@
 band_sig = filter_signal(sig_delta_ap, s_rate, 'bandpass', bands.beta)
 
 # Plot the filtered time series
-plot_time_series(times, band_sig)
-plt.xlim(0, n_seconds); plt.ylim(-1, 1);
+plot_time_series(times, band_sig, xlim=(0, n_seconds), ylim=(-1, 1))
 
 ###################################################################################################
 #
@@ -177,8 +176,7 @@
 band_sig = filter_signal(sig_delta_ap, s_rate, 'bandpass', bands.high_gamma)
 
 # Plot the filtered time series
-plot_time_series(times, band_sig)
-plt.xlim(0, n_seconds); plt.ylim(-1, 1);
+plot_time_series(times, band_sig, xlim=(0, n_seconds), ylim=(-1, 1))
 
 ###################################################################################################
 #