  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.
