diff --git a/examples/workflows/plot_simulate_alpha.py b/examples/workflows/plot_simulate_alpha.py
index bf41a8b60..79424a3d7 100644
--- a/examples/workflows/plot_simulate_alpha.py
+++ b/examples/workflows/plot_simulate_alpha.py
@@ -80,7 +80,8 @@
 
 plot_psd(dpl[trial_idx], fmin=1., fmax=1e3, tmin=tmin, ax=axes[1], show=False)
 axes[1].set_xscale('log')
-plt.tight_layout()
+
+hjhj
 ###############################################################################
 # The next step is to add a simultaneous 10 Hz :term:`distal` drive with a
 # lower within-burst spread of spike times (``burst_std``) compared with the
diff --git a/hnn_core/cell.py b/hnn_core/cell.py
index aa2168415..329c1e6b2 100644
--- a/hnn_core/cell.py
+++ b/hnn_core/cell.py
@@ -241,14 +241,15 @@ class Cell:
         Keys are name of synaptic mechanism. Each synaptic mechanism
         has keys for parameters of the mechanism, e.g., 'e', 'tau1',
         'tau2'.
-    topology : list of list
+    topology : list of list | None
         The topology of cell sections. Each element is a list of
         4 items in the format
         [parent_sec, parent_loc, child_sec, child_loc] where
         parent_sec and parent_loc are float between 0 and 1
         specifying the location in the section to connect and
         parent_sec and child_sec are names of the connecting
-        sections.
+        sections. If None, no topology is specified. This may
+        be necessary for single-compartment cells.
     sect_loc : dict of list
         Can have keys 'proximal' or 'distal' each containing
         names of section locations that are proximal or distal.
diff --git a/hnn_core/network.py b/hnn_core/network.py
index 777406d1b..54c75cc1e 100644
--- a/hnn_core/network.py
+++ b/hnn_core/network.py
@@ -1074,7 +1074,17 @@ def add_tonic_bias(self, *, cell_type=None, amplitude=None,
         }
 
     def _add_cell_type(self, cell_name, pos, cell_template=None):
-        """Add cell type by updating pos_dict and gid_ranges."""
+        """Add cell type by updating pos_dict and gid_ranges.
+    
+        Parameters
+        ----------
+        cell_name : str
+            The name of the cell population.
+        pos : list of tuple (x, y, z)
+            The position of the cells to be added to net.pos_dict.
+        cell_template : None | instance of Cell
+            An instance of            
+        """
         ll = self._n_gids
         self._n_gids += len(pos)
         self.gid_ranges[cell_name] = range(ll, self._n_gids)
diff --git a/hnn_core/viz.py b/hnn_core/viz.py
index bcca880ff..88854be36 100644
--- a/hnn_core/viz.py
+++ b/hnn_core/viz.py
@@ -543,10 +543,24 @@ def plot_cells(net, ax=None, show=True):
         fig = plt.figure()
         ax = fig.add_subplot(111, projection='3d')
 
-    colors = {'L5_pyramidal': 'b', 'L2_pyramidal': 'c',
-              'L5_basket': 'r', 'L2_basket': 'm'}
-    markers = {'L5_pyramidal': '^', 'L2_pyramidal': '^',
-               'L5_basket': 'x', 'L2_basket': 'x'}
+    #colors = {'L5_pyramidal': 'b', 'L2_pyramidal': 'c',
+              #'L5_basket': 'r', 'L2_basket': 'm'}
+
+   #markers = {'L5_pyramidal': '^', 'L2_pyramidal': '^',
+            #   'L5_basket': 'x', 'L2_basket': 'x'}
+            
+    all_colors = ['b', 'c', 'r', 'm', 'tab:orange', 'g', 'y', 'k', 'w', 'tab:brown']
+    all_markers= ['^', '^', 'x', 'x', 'o', 'v', 's', 'p', 'h', '<']
+    cell_names = ['L5_pyramidal', 'L2_pyramidal', 'L5_basket', 'L2_basket']
+    for cell_name in net.cell_types:
+        if cell_name not in cell_names:
+            cell_names.append(cell_name)
+    
+    colors= dict()
+    markers= dict()
+    for idx, cell_name in enumerate(cell_names):
+        colors[cell_name] = all_colors[idx]
+        markers[cell_name] = all_markers[idx]
 
     for cell_type in net.cell_types:
         x = [pos[0] for pos in net.pos_dict[cell_type]]
diff --git a/test_script.py b/test_script.py
new file mode 100644
index 000000000..f9d4d0f02
--- /dev/null
+++ b/test_script.py
@@ -0,0 +1,66 @@
+import hnn_core
+from hnn_core import  jones_2009_model, simulate_dipole, Network
+import numpy as np
+import matplotlib as plt
+
+params = hnn_core.read_params('/home/mohamed/Desktop/PhD Thesis/auditory_evoked_simulation/HNN-AEF-main/HNN_Parameters/L_Contra.param')
+net = jones_2009_model()
+
+
+def martinotti_template():
+    from hnn_core.cell import Section, Cell
+
+    cell_name = 'martinotti'
+    pos = (5, 5, 5)
+    # pos = net.pos_dict['L5_basket'].copy()
+
+    end_pts = [[0, 0, 0], [0, 0, 39.]]
+    sections = {'soma': Section(L=39., diam=20., cm=0.85,
+        Ra=200., end_pts=end_pts)}
+    sections['soma'].syns = ['gabaa', 'nmda']
+    
+    synapses = {
+        'gabaa': {
+            'e': -80,
+            'tau1': 0.5,
+            'tau2': 5.
+        },
+        'nmda': {
+            'e': 0,
+            'tau1': 1.,
+            'tau2': 20.
+        }
+    }
+    sect_loc = dict(proximal=['soma'], distal=['soma'])
+
+    return Cell(cell_name, pos,
+            sections=sections,
+            synapses=synapses,
+            topology=None,
+            sect_loc=sect_loc,
+            gid=None)
+
+x1= np.linspace(0, 7, 7)
+y1= np.linspace(0, 5, 5)
+xv, yv = np.meshgrid(x1, y1)
+pos = list()
+for (x, y) in zip(xv.ravel(), yv.ravel()):
+    pos.append((x, y, 0.))
+
+# net._add_cell_type('L5_martinotti', pos= pos, cell_template=martinotti_template())
+# net.plot_cells()
+
+weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,
+                   'L5_pyramidal': 0.080074}
+synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,
+                      'L5_pyramidal': 0.1}
+net.add_evoked_drive(
+    'evdist1', mu=63.53, sigma=3.85, numspikes=1, weights_ampa=None,
+    weights_nmda=weights_nmda_d1, location='distal',
+    synaptic_delays=synaptic_delays_d1, event_seed=4)
+
+simulate_dipole(net, tstop = 100, record_vsec=False)
+
+# net.add_connection(src_gids='L5_martinotti', target_gids='L5_pyramidal', loc='apical_tuft', receptor='gabaa', weight= 0.025 , delay=1.0 ,lamtha=70.0 , allow_autapses= False, probability=1)
+
+    
\ No newline at end of file