  It has been shown that a neural network model recently proposed to describe
basic memory performance is based on a ternary/binary coding/decoding algorithm
which leads to a new neural network assembly memory model (NNAMM) providing
maximum-likelihood recall/recognition properties and implying a new memory unit
architecture with Hopfield two-layer network, N-channel time gate, auxiliary
reference memory, and two nested feedback loops. For the data coding used,
conditions are found under which a version of Hopfied network implements
maximum-likelihood convolutional decoding algorithm and, simultaneously, linear
statistical classifier of arbitrary binary vectors with respect to Hamming
distance between vector analyzed and reference vector given. In addition to
basic memory performance and etc, the model explicitly describes the dependence
on time of memory trace retrieval, gives a possibility of one-trial learning,
metamemory simulation, generalized knowledge representation, and distinct
description of conscious and unconscious mental processes. It has been shown
that an assembly memory unit may be viewed as a model of a smallest inseparable
part or an 'atom' of consciousness. Some nontraditional neurobiological
backgrounds (dynamic spatiotemporal synchrony, properties of time dependent and
error detector neurons, early precise spike firing, etc) and the model's
application to solve some interdisciplinary problems from different scientific
fields are discussed.
