A single homogeneous layer of neural network is reviewed. For optical computing, a vector outer product model of neural network is fully explored and is characterized to be quasi-linear (QL). The relationships among the hetero-associative memory [AM], the ill-posed inverse association (solved by annealing algorithm Boltzmann machine (BM)), and the symmetric interconnect [T] of Hopfield's model E(N) are found by applying Wiener's criterion to the output feature f and setting [EQUATION].
"Three Layers Of Vector Outer Product Neural Networks For Optical Pattern Recognition", Proc. SPIE 0634, Optical and Hybrid Computing, (13 February 1986); doi: 10.1117/12.964021; https://doi.org/10.1117/12.964021