I was hoping I could get away with just ad hoc testing for a bit, but issues are proving a little too nuanced to efficiently root out without a more systematic approach. As a result, I started writing some unit tests (https://github.com/silasray/aiexplore/commit/95ae796dfeebb812f4d98e3887b2b832eff04ab3) with pytest to try to nail down expected behavior component by component before trying to analyze integrated behavior. In working on these tests, I also realized that something that I had convinced myself wasn’t an issue actually is.

I have mismatched design assumptions in the interrogate logic. I tried to be a little efficient by terminating traversal when a neuron is fully activated (https://github.com/silasray/aiexplore/blob/main/net.py#L36), but the function of the downstream neurons is cumulatively additive instead of marginally additive (https://github.com/silasray/aiexplore/blob/main/net.py#L68). I need to switch to a marginally additive system anyway, because right now, I think it’s producing runaway activation loops, but that means adding activation linked tracked on the synapses, not just the neurons. I guess that’s the next thing to do then.