> For the complete documentation index, see [llms.txt](https://elishai.gitbook.io/retinal-stimulation-modeling-environment/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://elishai.gitbook.io/retinal-stimulation-modeling-environment/sac-plexus-model/model-simulation.md).

# Model Simulation

Similar to the single SAC example you can simulate, trace the voltage at the soma and plot it:

```
simulation.simulate()
results = simulation.trace_voltages_at_soma()

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8,5))
for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]:
    plt.plot(results[0]['0'][i]['t'], results[0]['0'][i]['v'], ls='-', c='r', label='Expanding stimulus',linewidth=3)
plt.show()
```

Note that here voltages are traced in all 13 cells:

![](/files/HgwivGhlGzRpPUYE9i1f)

You can use the code below to generate the following synapse activity video, visualized over the SAC plexus:

{% file src="/files/ryIL2cTUEInCTPCWLrwE" %}

Note that the following code is not a native RSME code and that it might require the installation of video codecs in accordance with your operating system.

```
import seaborn as sns
import pandas as pd
from matplotlib import animation, rc
from IPython.display import HTML
from neuron import h
import matplotlib as mpl

xs = []
ys = []
sps = []

for cell in simulation.synapse_dict['light']['0']:
    for i in range(len(simulation.synapse_dict['light']['0'][cell]['x'])):
        x = simulation.synapse_dict['light']['0'][cell]['x'][i]
        y = simulation.synapse_dict['light']['0'][cell]['y'][i]
        syn = simulation.synapse_dict['light']['0'][cell]['BC_syn'][i]
        spike_times = simulation.simulation['spikes']['0'][cell][i]
        for sp_time in spike_times:
            xs.append(x)
            ys.append(y)
            sps.append(sp_time)

mpl.rcParams['animation.embed_limit']= 100
df = pd.DataFrame({'x':xs,'y':ys,'sps':sps})
df.index = df.sps
df = df.sort_index()

fig = figure(figsize=(9,9))
ax1 = subplot(1,1,1)
color = sns.color_palette("Paired",14)
for cell in simulation.simulation['cells']['0']['instances']:
    for sec in simulation.simulation['cells']['0']['instances'][cell]['section_dict']['section_list']:
        xs2 =[]
        ys2 =[]
        for i in range(int(sec.n3d())):
            xs2.append(sec.x3d(i))
            ys2.append(sec.y3d(i))
        plot(xs2, ys2,color=color[cell-1])
    plt.axis('equal')

old_sps =scatter([0],[0],marker='o')
def show_spikes(dt):
    global time, old_sps
    title(f't = {time} ms')
    sps = df.loc[time:time+dt]
    old_sps.remove()
    scs = scatter(sps.x,sps.y,marker='o', color='k')
    old_sps = scs
    time +=dt
    return []

time = 0
anim = animation.FuncAnimation(fig, show_spikes, frames=[1] + [4]*200)
Writer = animation.writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='NBEL'), bitrate=1800)
anim.save('plexus_SAC_stimulus.mp4', writer=writer)
HTML(anim.to_jshtml())
```