1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
|
.. _example_of_generating_movie:
Example of Generating a Movie
============
`ipynb from google colab <https://colab.research.google.com/drive/1YuDWJt2osDaAqVlMKZ2AZBhqrabWDmjz?usp=sharing#scrollTo=Gn45M0sd_GlD>`_ to google colab
Install NEURON, update matplotlib
----------
The matplotlib update lets us specify ``vertical_axis``
.. code::
python
!pip install neuron
!pip install --upgrade matplotlib
Load the morphology definition
-----------
.. code::
python
!wget https://raw.githubusercontent.com/ramcdougal/dentategranulevideo/master/n275.hoc
Setup the model
--------
.. code::
python
from neuron import h
import matplotlib.pyplot as plt
import tqdm
from IPython.display import HTML
from base64 import b64encode
from neuron.units import ms, mV
plt.rcParams["figure.figsize"] = (6, 6)
h.load_file("stdrun.hoc")
h.load_file("n275.hoc")
.. code::
python
STOP_TIME = 45 * ms
SAVE_EVERY = 0.25 * ms
.. code::
python
for sec in h.allsec():
sec.nseg = 21
if 'dend' not in sec.name():
sec.insert(h.hh)
.. code::
python
# setup current pulses to trigger APs
fire_times = [0 * ms, 15 * ms, 23 * ms, 31 * ms]
iclamps = []
for time in fire_times:
iclamp = h.IClamp(h.soma[0](0.5))
iclamp.delay = time
iclamp.amp = 2
iclamp.dur = 0.5 * ms
iclamps.append(iclamp)
Simulation control and image saving
------------
.. code::
python
def neuron_images():
# rotation
ps = h.PlotShape(False)
ps.plot(plt)
for theta in range(0, 360, 9):
plt.gca().view_init(0, theta, vertical_axis="y")
yield
plt.close()
ps.variable("v")
ps.scale(-80, 50)
# now let's run the sim, plt on a new figure every SAVE_EVERY, then yield
h.finitialize(-65 * mV)
for i in range(200):
h.continuerun(i * SAVE_EVERY)
ps.plot(plt)
if i < 40:
theta = 9 * i
else:
theta = 0
plt.gca().view_init(0, theta, vertical_axis="y")
yield
plt.close()
.. code::
python
def save_all_images():
for i, _ in tqdm.tqdm(enumerate(neuron_images())):
plt.savefig(f"{i:04d}.png")
This counts to 40 + 200 = 240. Runs in about twelve minutes. The first 40 is relatively fast because it's only rotating an existing plot, not simulating or making a new plot.
.. code::
python
save_all_images()
Put it all together into an MP4
------------
.. code::
python
!ffmpeg -r 20 -i %04d.png -vcodec libx264 -crf 25 -pix_fmt yuv420p neuron_movie.mp4
Let's look at the MP4
-------------
.. code::
python
# adapted from https://stackoverflow.com/questions/57377185/how-play-mp4-video-in-google-colab
def show_video(video_path):
with open(video_path, "r+b") as f:
video_url = f"data:video/mp4;base64,{b64encode(f.read()).decode()}"
return HTML(f"<video width=640 controls><source src='{video_url}'></video>")
.. code::
python
show_video("neuron_movie.mp4")
|
