Principles of the design of D-neuronal networks

Abstract

A self-learning neuronal network called MACSIM (Mathematical Analyzer and Composer of Simple Melodies) for music pattern recognition is proposed. As universal network elements the D-neurons defined in the previous paper (Fedor and Majernik, 1977) are used. MACSIM is intended to model, at the level of elementar neuronal circuits, the processing of simple melodies which takes place in the human brain auditory system. Melody composing and music itself are considered in this study in order to verify the principles of design and functioning of the proposed basic D-neuronal networks (organs) of MACSIM, only. Two kinds of neuronal organs are incorporated in MACSIM, namely the so-called analyzers and the so-called neuro-effectors. The first ones carry out predicting, learning and decoding of the input sequences of tones. An analyzer consists of several neuronal trees that have the ability to specialize and respecialize so that every melody repeated several times can be recognized after a corresponding learning period. Such a self-learning process runs without structural changes within the mentioned networks. A melody which has been memorized in an analyzer can be memorized later in a neuro-effector, namely in a form suitable for its recall at the output of MACSIM. The neuronal organization of the neuro-effectors is similar, to a certain extent, to that of the cerebellar cortex. With the help of the staggered arrangement of D-neurons set up on the lines of the organization of Purkinje cells in the cerebellar cortex, a new reliable memory storage mechanism is proposed. This paper is not self-contained; the reader is assumed to be familiar with the above-mentioned paper.