  Biological neurons have adaptive nature and perform complex computations
involving the filtering of redundant information. However, most common neural
cell models, including biologically plausible, such as Hodgkin-Huxley or
Izhikevich, do not possess predictive dynamics on a single-cell level.
Moreover, 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. The present article introduces new mechanics of interconnection
between neuron firing rate homeostasis and weight change through STDP growth
bounded by abstract protein reserve, controlled by the intracellular
optimization algorithm. We show how these cellular dynamics help neurons filter
out the intense noise signals to help neurons keep a stable firing rate. We
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.
