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.

生物神经元具有自适应特性并执行涉及过滤冗余信息的复杂计算。然而，大多数常见的神经细胞模型，包括生物学上合理的模型，如 Hodgkin-Huxley 或 Izhikevich，都不具备单细胞水平的预测动力学。此外，突触可塑性或互连权重适应的现代规则也没有为神经元适应不断变化的输入信号强度的能力提供基础。虽然自然神经元突触的生长受到蛋白质供应和回收的精确控制和限制，但广泛使用的 STDP 等权重校正规则在变化率和规模上有效地不受限制。本文介绍了神经元放电率稳态与体重变化之间相互关联的新机制，通过受抽象蛋白质储备限制的 STDP 增长，由细胞内优化算法控制。我们展示了这些细胞动力学如何帮助神经元过滤掉强烈的噪声信号，从而帮助神经元保持稳定的放电率。我们还检查了这种过滤不会影响神经元在无监督模式下识别相关输入的能力。这种方法可用于机器学习领域，以提高人工智能系统的鲁棒性。我们还检查了这种过滤不会影响神经元在无监督模式下识别相关输入的能力。这种方法可用于机器学习领域，以提高人工智能系统的鲁棒性。我们还检查了这种过滤不会影响神经元在无监督模式下识别相关输入的能力。这种方法可用于机器学习领域，以提高人工智能系统的鲁棒性。