So far, I have sucessfully managed to implement a one populatuion model and estimate that its behaviour is consistent. I have done this by measuring the spikes generated by the poisson generators (input of the population) and the spikes generated by the population itself (output of the population).

The results from the latter step need to be checked against theory. The formulea to do so is extremely complex and I am expecting some software from Marc to help with these calculations. I was able to calculate the input rate as these formulae are relatively simple.

I have implemented (I believe) a two population model, with both excitatory and inhibitory populations. The model has a poisson generator for each excitatory neuron. These provides the initial stimulus for the excitatory population. The excitatory and inhibitory populations each have 0.1 * the population size, random connections to other neurons (self or cross population). Each population also has a spike detector attached.

Using the same variable values as previously used for the one population model, this yielded very little activity (17 ex-spikes and 0 in-spikes). However, if the weights are tweaked upwards, I found that the excitatory population started off barely spiking, until about 40ms where its rate increased exponetially to 50000 before 50ms, after which it maintained this firing rate. The inhibitory population displayed similar behaviour but on a smaller scale (as we would expect with a smaller population).

The resulting histograms derived from these spiking data clearly show a sigmoid shape. This indicates stable regular firing, though I have yet to confirm this.