The solving of least square systems is a useful operation in neurocomputational modeling of learning, pattern matching, and pattern recognition. In these last two cases, the solution must be obtained on-line, thus the time required to solve a system in a plausible neural architecture is critical. This paper presents a recurrent network of Sigma-Pi neurons, whose solving time increases at most like the logarithm of the system size, and of its condition number, which provides plausible computation times for biological systems.
@article{arxiv.0804.4808,
title = {Solving Time of Least Square Systems in Sigma-Pi Unit Networks},
author = {Pierre Courrieu},
journal= {arXiv preprint arXiv:0804.4808},
year = {2008}
}