Jin Ohta

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An approach for analysis of biological networks is proposed. In this approach, named the connectivity matrix (CM) method, all the connectivities of interest are expressed in a matrix. Then, a variety of analyses are performed on GNU Octave or Matlab. Each node in the network is expressed as a row vector or numeral that carries information defining or(More)
An optical associative neural network with a stochastic thresholding procedure has been demonstrated. The use of stochastic processing drastically improved the convergence rate into the correct global minima (recognition rate). The properties of undesirable spurious minima were also investigated. It was found that the spurious minima were represented as the(More)
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