  Biological neurons have adaptive nature and perform complex computations
involving the filtering of redundant information. Such processing is often
associated with Bayesian inference. Yet most common models of neural cells,
including biologically plausible, such as Hodgkin-Huxley or Izhikevich do not
possess predictive dynamics on the level of a single cell. The modern rules of
synaptic plasticity or interconnections weights adaptation also do not provide
grounding for the ability of neurons to adapt to the ever-changing input signal
intensity. While natural neuron synaptic growth is precisely controlled and
restricted by protein supply and recycling, weight correction rules such as
widely used STDP are efficiently unlimited in change rate and scale. In the
present article, we will introduce new mechanics of interconnection between
neuron firing rate homeostasis and weight change by means of STDP growth
bounded by abstract protein reserve, controlled by the intracellular
optimization algorithm. We will show, how these cellular dynamics help neurons
to filter out the intense signals to help neurons keep a stable firing rate. We
will also examine that such filtering does not affect the ability of neurons to
recognize the correlated inputs in unsupervised mode. Such an approach might be
used in the machine learning domain to improve the robustness of AI systems.
