Solving Time of Least Square Systems in Sigma-Pi Unit Networks

authors

  • Courrieu Pierre

keywords

  • Least Square Systems
  • On-line Pattern Matching
  • RBFN Learning
  • Sigma-Pi Neurons
  • Recurrent Neural Network

document type

ART

abstract

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.

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