> a job interview with a deep learning startup

I am not aware of any DL startups that are interested in techniques which are incompatible with GPUs and back propagation. I think this is a solo journey through the dark forest for now.

Respect to the OP as spiking networks are a different domain, but the field of Graceful Degradation and Perturbations itself is almost older than the field of Neural Networks itself. Generally, the bitter lesson occurs - these techniques show some kind of useful robustness at parameter M, but fail to show as much of a useful robustness relatively as parameter M*100. For example, before attention, using Drop out (in essence a form of forcing robustness) was one of the most common techniques. The quality of robustness or its resulting sub parts (e.g. the tradeoff between precision and recall) is incorporated as a measure in some of the fields basic scores, such as harmonic F1 score.

Still it, like alternate activation functions, data beyond matrices and vectors and non-linear networks, remain very unstudied.

Your surprise breaks my brain. Why would someone "keep it to themselves" outside some white paper they won't have time to promote or for a job interview that they might not need?

Is your thought that it is some novel approach that nobody is doing and has obvious market implications? Even then, the implications matter only if you are in a position to take advantage.

The Internet used to be a place people shared cool shit because it was cool, not for some monetization scheme or personal gain beyond the joy of sharing. Content creators (as a job) and hustle culture are a blight.