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.
翻译:生物神经元具有适应性,并进行包括过滤多余信息在内的复杂计算。然而,大多数常见神经细胞模型,包括Hodgkin-Huxley或Izhikevich等在生物上可信的神经细胞模型,并不具有单细胞一级的预测动态。此外,合成可塑性或互联重量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提供依据。虽然天然神经神经合成增长受到蛋白质供应和再循环的精确控制和限制,但广泛使用的STDP等重量校正规则在变化速度和规模上是有效的无限制的。本文章介绍了神经射电率自闭和通过细胞内部优化算法控制的抽象蛋白储备的重量变化之间的相互联系新机制。我们展示了这些细胞动态如何帮助神经元过滤强烈的噪音信号,以帮助神经元保持稳定的发射速度。我们还研究,这种过滤不会影响神经元识别非超超强模式中相关输入的能力。这种方法可能在机器域内应用来改进坚固的AI。