Both S. L. Thaler and R. Hecht-Nielsen have set forth neural-based theories of "confabulation" applicable to machine learning.
The essential mathematics of Hecht-Nielsen is set forth in his paper "Cogent Confabulation". Briefly it is an inversion of Bayesian inference. Bayesian inference is P(x|a&b&c&d...) where one is estimating the probability of x assuming a, b, c, d, etc. Its inversion is P(a&b&c&d...|x), which RHN calls the "cogency" of x given the assumptions.
I haven't seen a similarly succinct description of Thaler's mathematics that would permit comparison to see if the theories are isomorphic. Are they?