 
Summary: I. Aizenberg. Computational Intelligence, Theory and Application" (B. Reusch Editor),
Springer, Berlin, Heidelberg, New York, 2006, pp. 457471
Solving the Parity n Problem and Other Nonlinearly
Separable Problems Using a Single Universal Binary
Neuron
Igor Aizenberg
Texas A&M UniversityTexarkana,
P.O. Box 5518, 2600 N. Robison Rd. Texarkana, Texas 75505 USA;
igor.aizenberg@tamut.edu
Abstract. A universal binary neuron (UBN) operates with the complexvalued
weights and the complexvalued activation function, which is the function of
the argument of the weighted sum. This makes possible the implementation of
the nonlinearly separable (nonthreshold) Boolean functions on the single
neuron. Hence the functionality of the UBN is incompatibly higher than the
functionality of the traditional perceptron, because this neuron can implement
the nonthreshold Boolean functions. The UBN is closely connected with the
discretevalued multivalued neuron (MVN). This is also a neuron with the
complexvalued weights and the complexvalued activation function, which is
the function of the argument of the weighted sum. A close relation of the MVN
and UBN and of the multiplevalued threshold functions and Prealizable
