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single_unit.md

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Extracting and plotting single unit data

Take a look at the complete data

In assignment 4, I created a DataFrame called data, which contains the data collected from each probed neuron during each trial.

print(data.sample(20))

       neuron  spiketime  repetition  contrast condition  trial
12415   m2_13        709           5        64     ADAPT  12415
25908   m4_17       2606           7        64     ADAPT  25908
26274   m4_17         90           7        84     ADAPT  26274
12913   m2_13       1018           1       100     ADAPT  12913
22095   m4_17       2797           7        24      CTRL  22095
14277    m3_3       2836           1        48      CTRL  14277
29737  m6_17b       2125           3        16     ADAPT  29737
29013  m6_17b       1504           1         8     ADAPT  29013
30923  m6_17b       1063           2        64     ADAPT  30923
28668  m6_17b       2143           8       100      CTRL  28668
1294     m1_6        643           8        32     ADAPT   1294
1988     m2_9       1559           2         4      CTRL   1988
2581     m2_9       2374           7        12      CTRL   2581
22780   m4_17       2626           8        64      CTRL  22780
27479  m6_17b       2608           5        48      CTRL  27479
2567     m2_9       2052           7        12      CTRL   2567
10269   m2_13       2492           8        84      CTRL  10269
15319    m3_3       2418           7       100      CTRL  15319
1635     m1_6        269           1        84     ADAPT   1635
27280  m6_17b       2217           4        32      CTRL  27280

Look at the data from a single neuron

To be able to plot the data from a single neuron I first had to extract the data from only that neuron from the DataFrame. I further extracted the trials where the contrast was 48% and it was under the control condition.

dat = data[(data['neuron'] == 'm1_6') & (data['condition'] == 'CTRL') & (data['contrast'] == 48)]
print(dat)

    neuron  spiketime  repetition  contrast condition  trial
85    m1_6       1504           1        48      CTRL     85
86    m1_6       2361           1        48      CTRL     86
87    m1_6       2374           1        48      CTRL     87
88    m1_6       2508           1        48      CTRL     88
89    m1_6       2657           1        48      CTRL     89
..     ...        ...         ...       ...       ...    ...
152   m1_6       2602           8        48      CTRL    152
153   m1_6       2634           8        48      CTRL    153
154   m1_6       2689           8        48      CTRL    154
155   m1_6       2717           8        48      CTRL    155
156   m1_6       2838           8        48      CTRL    156

[72 rows x 6 columns]

As you can see, the DataFrame dat contains only data from neuron m1_6

Raster plot

Next I created a raster plot when m1_6 spiked under the above conditions.

fig, ax = plt.subplots()
ax.vlines(dat['spiketime'], 0, 1)

ax.set_title('Neuronal Spike Times') 
ax.set_xlabel('Time (ms)')

plt.show()

png.

Peri-Stimulus Time Histogram (PSTH)

Here, I plotted the PSTH of when m1_6 spiked under the above conditions. I included grey shading during the time range when the stimulus was running

fig, ax = plt.subplots()

ax.axvspan(stim_on_time, stim_off_time, alpha = 0.5, color = 'gray')

ax.hist(data['spiketime'], bins = 100)

ax.set_title('Peri-Stimulus Time Histogram (PSTH)')
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Num. spike occurrences at this time')

plt.show()

png.