Tuning curves are commonly used to characterize the properties of sensory and motor neurons, and while they appear simple, researchers actually take several steps to create them.
June 22, 2017—Monica Linden
Description of the Data
Using extracellular electrophysiology, a researcher will record activity from one neuron while varying a parameter of interest. In our example, we will explore data from Professor John Donoghue’s laboratory at Brown University. The researcher recorded from a neuron in primary motor cortex (area M1) while the animal held a manipulandum (a joystick) and moved his arm toward a target in one of 8 directions evenly spaced around a circle on a flat plane in front of the animal. Trials of different directions are generally interwoven in a pseudorandom fashion. The animal completed 17 trials per direction.
RawData
Process Data
The data was provided by the researcher and needs to be converted into a more useful format for visualization. The data came in a CSV file with four columns, where each row corresponds with an action potential (spike) recorded from the neuron. Column 1 is the actual time of the spike; Column 2 the trial direction; Column 3 the trial number; and Column 4 the time of the spike centered around the time the animal began moving its arm. The data contains 8 directions of motion with 17 trials per direction.
We can extract the directions of motion from the data. Here the directions are in Column 2, and Union will extract all values and remove duplicates:
In[]:=
directions=Union[Map[Part[#,2]&,rawdata]]
Out[]=
{0,45,90,135,180,225,270,315}
Next, we group the trials for each direction. This can be used in the additional steps. Briefly, the spike times are sorted by direction and trial number:
Determine the effect of bin size on the shape of the PSTH. Why should bin size always be greater than 1 ms? What happens if your bin size is too large?
Represent the tuning curve as a polar plot. Why is this a useful representation?
Explore what you can and cannot predict from the tuning curve. For example, if you know the firing rate of the neuron, can you predict the value of the parameter of interest?