Time and Input

The model (unlike real neurons) is discrete, so it has cycles.
We've equated a cycle with 10ms of real time.
This is somewhat arbitrary, but it enables us to avoid properties like absolute and relative refractory periods and synaptic delay as they occur in less than 10ms.
Also neurons usually don't spike more than once every 10ms.
Input is either from the environment (the simulation just adds activity) or from other neurons. When a neuron spikes the activity arrives at the post-synaptic neurons in the next cycle.
In the biological data and in this model, activation comes directly from the environment.
So, now we're going to get some data on neural spikes, and see how well the FLIF model can fit the data.