An Expansion of the Neural Network Theory by Introducing Hebb Postulate

Article type: Research Article

Authors: Garliauskas, Algis

Affiliations: Institute of Mathematics and Informatics, Akademijos 4, LT-06883 Vilnius, Lithuania, e-mail: galgis_1@ktl.mii.lt

Abstract: In the presented paper, some issues of the fundamental classical mechanics theory in the sense of Ising physics are introduced into the applied neural network area. The expansion of the neural networks theory is based primarily on introducing Hebb postulate into the mean field theory as an instrument of analysis of complex systems. Appropriate propositions and a theorem with proofs were proposed. In addition, some computational background is presented and discussed.

Keywords: Hamiltonian, Ising model, Hebb postulate, neural network, memory capacity

Journal: Informatica, vol. 21, no. 3, pp. 339-348, 2010