Reyes Marcelo Bussotti, Huerta Ramon, Carelli Pedro Valadão, Pinto Reynaldo D, Rabinovich Mikhail I, Selverston Allen I
Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Santo André, Brasil.
BioCircuits Institute, University of California, San Diego, La Jolla, California 92093, USA.
bioRxiv. 2025 Jul 27:2025.07.23.666219. doi: 10.1101/2025.07.23.666219.
The stability of rhythmic activity in neural networks is an important aspect in the study of central pattern generators (CPGs). Different from other physiological rhythms, the activity of CPGs has not been fully characterized in terms of its stability, especially using quantitative methods. We propose a method that takes advantage of the natural noise present in CPGs to quantify the stability of the rhythmic activity. Furthermore, we used the stationary bootstrap method to define confidence intervals of the results. We applied this method to study the influence of a synaptic modification on the pyloric CPG circuit, using artificial synapses implemented in dynamic clamp software. We show that even after removing one of its strongest synapses, the CPG stability remains unaltered. This analysis suggests that CPGs are designed to be strongly stable regardless of the parameter perturbations they undergo.
神经网络中节律性活动的稳定性是中枢模式发生器（CPG）研究中的一个重要方面。与其他生理节律不同，CPG的活动在稳定性方面尚未得到充分表征，尤其是使用定量方法。我们提出了一种利用CPG中存在的自然噪声来量化节律性活动稳定性的方法。此外，我们使用平稳自抽样方法来定义结果的置信区间。我们应用该方法研究突触修饰对幽门CPG回路的影响，使用动态钳制软件中实现的人工突触。我们表明，即使去除其最强的突触之一，CPG的稳定性仍保持不变。该分析表明，CPG被设计为具有很强的稳定性，无论它们经历何种参数扰动。
