[monocasa]

For some biological neural nets, one would imagine the loss function just being nearly completely genetic. That is, both the weights are ultimately stored in the genetic code the same way any other cell differentiation information is stored, and were converged on in the first place by random mutations being more or less fit for their ecological niche. Particularly this could explain the relatively fixed function components like our V1 and V2 regions, our motor region, and probably the entire nervous system of simpler animals.

Basically in a lot of cases evolution takes the place of back proposition, and large structures are what we'd call inference only without learning. Doesn't make them any less of neural nets.