As I mentioned at the end of my Round 2 answer, I've needed to expand my code in order to produce faithfully the data needed for Figure 1 of this paper.

My code simulates a network of 128 leaky integrate-and-fire neurons for 10 seconds. If you look at the code posted with my Round 1 and Round 2 questions, you'll see that in those rounds I kept track of only the most recent spike (action potential) of each neuron, in a variable called t_spike_array. t_spike_array had length N = 128; it contained the time of the most recent spike for each neuron in the population.

As I mentioned at the end of my Round 2 answer, I've needed to expand my code in order to produce faithfully the data needed for Figure 1 of this paper.

My code simulates a network of 128 leaky integrate-and-fire neurons for 10 seconds. If you look at the code posted with my Round 1 and Round 2 questions, you'll see that in those rounds I kept track of only the most recent spike (action potential) of each neuron, in a variable called t_spike_array. t_spike_array had length N = 128; it contained the time of the most recent spike for each neuron in the population.

Confession

It's possible that my script is (silently) buggy. I know this site is not for debugging. Because my question regards what's making my code slow, not what's making the results incorrect -- indeed, I can't even get results due to the slowness -- I have seen it fit to post here.

(It's possible that a bug is what's making the code slow, but I believe that getting at the root of the speed issue qua speed issue is what's needed -- again justifying a post here.)

3. In the script below, I have n_x = 2. To produce the full dataset needed for Figure 1, I would need to set n_x to something like 100. This of course would add substantial processing time (hence why I'm using the smaller value as I refactor).