Assume there exists a behavior of an organism one wants to model in a robot.
Assume further, there exist properties of the underlying structure, usually neurons, you hypothesize the behavior to be based on.

Know this: A solution can not be more interesting that the problem posed.
Again, differently: How interesting a solution is, is bounded by the problem.

To make a little robot avoid walls and to follow lights, mostly any property of a neuron will do. Those include delays, multiplicative synapses, spiking neurons, channel plasticity, moving dendrites, large networks, neuromodulators, different time scales, field potentials, and the list goes and goes. All afford equally interesting solutions.

Each not really very much, though.

Einstein said, and everyone knows: " make your solution as simple as possible but not simpler".

He could be talking about modeling of neural systems.

Are GMO's (genetically modified organisms) good or bad?
Truth is, this is a case-based case. Decision depends on the case under gaze.

Problem: the outcome can only be seen when it's conspicuously there. We may discuss all the potential danger, but until one turns out to be the case, it is just an exercise in conjecturing.

But. We humans cluster judgements - from conjectures into laws - because if we don't generalize, we can't act.

So, inexorability of disaster: between the hypothesis of hunger (no GMOs) and worldwide intoxication (with GMOs) we will only know when they are there.

Makes our efforts in preventing them pretty pointless. Put us in the brink of disaster.

Sound the Alarms! Run like a mad man! Whatever direction!