Sign-representation of Boolean functions using a small number of monomials.
This paper presents a deterministic algorithm that can construct a higher-order neuron representation for an arbitrary n-variable Boolean function with a fan-in less than 0.75 x 2(n), and provides related theoretical results. When the logic constants True and False are identified by +1 and -1, an n-variable Boolean function is identified by a unique dichotomy of the n-dimensional hypercube. With this equivalence, all n-variable Boolean functions can be uniquely represented by linear combinations of monomials, the products of input variables. A polynomial function whose sign matches the truth table of a given Boolean function is said to sign-represent that Boolean function. The artificial neural units that implement this sign-representation scheme are often called higher-order neurons or polynomial threshold units. This paper investigates the freedom provided by the sign-representation framework in terms of the fan-in of these artificial neural units. In particular, we look for sign-representations with a small number of monomials. Although there are methods developed for finding a reduced set of monomials to represent Boolean functions, there are no deterministic algorithms for computing non-trivial solutions with guarantees on the number of monomials in the found sign-representations. This work fills this gap by providing deterministic algorithms which are guaranteed to find solutions with fewer than 0.75 x 2(n) monomials for n-variable Boolean functions. Although the algorithms presented here are computationally costly, it is expected that several research directions can be spawned from the current study, such as reducing the 0.75 x 2(n) bound and devising efficient algorithms for finding sign-representations with a small number of monomials.
Oztop
Erhan
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JST-ICORP Computational Brain Project, 4-1-8 Honcho Kawaguchi, Saitama, Japan. erhan@atr.jp
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D008128:Logic; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics