  Parallels between the signal processing tasks and biological neurons lead to
an understanding of the principles of self-organized optimization of input
signal recognition. In the present paper, we discuss such similarities among
biological and technical systems. We propose the addition to the well-known
STDP synaptic plasticity rule to directs the weight modification towards the
state associated with the maximal difference between the background noise and
correlated signals. The principle of physically constrained weight growth is
used as a basis for such control of the modification of the weights. It is
proposed, that biological synaptic straight modification is restricted by the
existence and production of bio-chemical 'substances' needed for plasticity
development. In this paper, the information about the noise-to-signal ratio is
used to control such a substances' production and storage and to drive the
neuron's synaptic pressures towards the state with the best signal-to-noise
ratio. Several experiments with different input signal regimes are considered
to understand the functioning of the proposed approach.
