Part 5: Generating fake spike train data. In Part 4, we generated artificial spike train data by flipping a biased coin 500 times. Let's consider this as a single 'trial' of spike train data. We can easily generate many trials of such synthetic data. Let's do it, and generate 200 trials of data, each trial consisting of 500 flips of our biased coin,

m = 1;

p = 0.01;

fakeSpikes2 = binornd(m,p,[200,500]);

IN LAB Q: Consider the variable 'fakeSpikes2' defined in the line above. We can think of 'fakeSpikes2' as representing multiple trials of spike train data. In this case, we generate 200 trials, each consisting of 500 coin flips. We can analyze these artificial data in the same way we analyzed the 'real' spike train data you To do so, let's assume that each coin flip represents a tiny interval of time,

1 coin flip = 1 ms interval of time

Each trial consists of 500 coin flips, or equivalently 500 ms. We can now compute quantities like the average firing rate, the spike number histogram, and the ISI histogram. You'll be asked to do so in this Week's assignment.

IMPORTANT NOTE: Using MATLAB, we can generate our own spike train data. This is extremely powerful. In doing so, we don't deal with a messy biological experiment. Instead, we're in complete control, and not at the mercy of the finicky biology of a neuron.

Need help finding the average firing rate!! With steps, please! (Just posted the description in case it helped)

Generate artifical spike train data following the procedure in Part 5 with a different value for p. More specifically, let's define the spike train data 'd' as: m = 1; p = 0.05; NOTE: different value for p compared to Lab. d = binornd(m,p, [200,500]); We consider the data as consisting of 200 trials. Each trial contains 500 time points. Let's assume the interval between time points is 1 millisecond, so each trial lasts 500 milliseconds (or equivalently half a second). Compute the average firing rate of these data. Generate artifical spike train data following the procedure in Part 5 with a different value for p. More specifically, let's define the spike train data 'd' as: m = 1; p = 0.05; NOTE: different value for p compared to Lab. d = binornd(m,p, [200,500]); We consider the data as consisting of 200 trials. Each trial contains 500 time points. Let's assume the interval between time points is 1 millisecond, so each trial lasts 500 milliseconds (or equivalently half a second). Compute the average firing rate of these data