  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 adding the well-known STDP
synaptic plasticity rule to direct the weight modification towards the state
associated with the maximal difference between background noise and correlated
signals. We use the principle of physically constrained weight growth as a
basis for such weights' modification control. It is proposed that the existence
and production of bio-chemical 'substances' needed for plasticity development
restrict a biological synaptic straight modification. In this paper, the
information about the noise-to-signal ratio controls such a substances'
production and storage and drives the neuron's synaptic pressures towards the
state with the best signal-to-noise ratio. We consider several experiments with
different input signal regimes to understand the functioning of the proposed
approach.
