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Alex Swain
Is Our Mouse Learning? Obtaining Data to Check!
In the lab of my current rotation, I am primarily working on training mice that are waterdeprived to lick a device for a water reward when a stimulus is present. The goal of this code,
therefore, is to obtain relevant data from the file generated from the training experiments to
assess how well the mouse is responding to the training. When taken over several days, there
should be several factors that imply that the mouse is indeed learning that:
1. It can lick a device (lickport) to obtain water, subsequently the mouse would be
inclined to lick more (total number of licks should increase over training days).
2. The mouse should lick during the reward stimulus correctly over time.
Original data output looks like this:
Messy, right?
122294 3
General Information About the Raw Data
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The leftmost numbers are timestamps (in milliseconds) of when a stimulus is
~~~
presented. 3 means that there was a random visual stimulus that flashed on
123330 4
the screen for 1 second (1000 milliseconds). The 4 means that, after the
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stimulus a grey screen was presented for 1 second. The 5 means that the
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reward stimulus flashed and (~150 ms later) the 6 means that, if the mouse
124394 5
licked the lickport, water would be presented to the mouse. (The time delay
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is to take account of the time it takes for visual information to be processed
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and subsequently acted upon by the mouse).
124590 6
124873 2 0
Following the timestamp + 6, a timestamp followed by a 2 means the device
124884 0 0
registered a lick. The 0 can be ignored as a technicality of the programs
124888 2 0
development and does not provide any relevant information. Water is only
~~~
presented on the first successful lick following the “6”.
Moving on to the project (which should take one argument – a text file of the training day!)
Within the experiment, there are 900 total stimuli trials and 900 grey screens. 100 of the stimuli
trials are reward stimuli. Therefore, to ensure the program ran correctly, you will want to
implement code to parse the information such that you ensure there are 100 reward stimuli
trials. This is simply to ensure, at least superficially, that the data is not corrupted.
Next you can obtain information on the TOTAL NUMBER OF LICKS the mouse obtained
throughout the entirety of the experiment (that is, timestamp with a 2 following it). After this,
you will want to parse the data such that you can get the number of licks the mouse received
water on (the first lick (2) after the (6)). With this you can obtain the total number of licks the
mouse obtained water on, as well as the mean and std. deviation. Perhaps it will show
interesting information over a prolonged period of training!?
Finally, plot a histogram of the total licks over time and save the figure. The pattern of the
histogram is reflective, as well, of the mouse’s learning.
Alex Swain
Ultimately you should be able to tell us:
1. The total number of reward stimuli (6)
2. The total number of licks the mouse took.
3. The number of licks that obtained water.
4. The mean lick time and its standard deviation, formatted to two decimal places.
5. A histogram of the total licks over time.
If the mouse is “learning” we should see the total number of licks increase (because it learns
that the lickport gives water), as well as the total number of licks it receives water in increasing.
A histogram can help us visualize the pattern of the mouse’s licks. Mean and std. deviation
should be reasonable as well, and maybe with enough data a trend will be observed showing
that the mouse is reacting faster too!
Alex Swain
Examples:
NOTE: The histograms are not printed but are saved as a file in the local directory.
./assign13.py Day1.txt
Total Reward Stimuli: 100
Total Licks: 685
Number of licks that obtained water: 3
Mean Lick Time: 123.67 +/- 58.62 ms
Alex Swain
./assign13.py Day2.txt
Day 2 Output:
Total Reward Stimuli: 100
Total Licks: 986
Number of licks that obtained water: 28
Mean Lick Time: 170.75 +/- 59.57 ms
Alex Swain
./assign13.py Day4.txt
Total Reward Stimuli: 100
Total Licks: 1851
Number of licks that obtained water: 100
Mean Lick Time: 174.87 +/- 41.99 ms