  A ternary/binary data coding algorithm and conditions under which Hopfield
networks implement optimal convolutional or Hamming decoding algorithms has
been described. Using the coding/decoding approach (an optimal Binary Signal
Detection Theory, BSDT) introduced a Neural Network Assembly Memory Model
(NNAMM) is built. The model provides optimal (the best) basic memory
performance and demands the use of a new memory unit architecture with
two-layer Hopfield network, N-channel time gate, auxiliary reference memory,
and two nested feedback loops. NNAMM explicitly describes the dependence on
time of a memory trace retrieval, gives a possibility of metamemory simulation,
generalized knowledge representation, and distinct description of conscious and
unconscious mental processes. A model of smallest inseparable part or an "atom"
of consciousness is also defined. The NNAMM's neurobiological backgrounds and
its applications to solving some interdisciplinary problems are shortly
discussed. BSDT could implement the "best neural code" used in nervous tissues
of animals and humans.
