More-Potdar Smita, Golowasch Jorge
Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ, United States.
Front Cell Neurosci. 2023 Dec 14;17:1280575. doi: 10.3389/fncel.2023.1280575. eCollection 2023.
Robustness of neuronal activity is a property necessary for a neuronal network to withstand perturbations, which may otherwise disrupt or destroy the system. The robustness of complex systems has been shown to depend on a number of features of the system, including morphology and heterogeneity of the activity of the component neurons, size of the networks, synaptic connectivity, and neuromodulation. The activity of small networks, such as the pyloric network of the crustacean stomatogastric nervous system, appears to be robust despite some of the factors not being consistent with the expected properties of complex systems, e.g., small size and homogeneity of the synaptic connections. The activity of the pyloric network has been shown to be stable and robust in a neuromodulatory state-dependent manner. When neuromodulatory inputs are severed, activity is initially disrupted, losing both stability and robustness. Over the long term, however, stable activity homeostatically recovers without the restoration of neuromodulatory input. The question we address in this study is whether robustness can also be restored as the network reorganizes itself to compensate for the loss of neuromodulatory input and recovers the lost activity. Here, we use temperature changes as a perturbation to probe the robustness of the network's activity. We develop a simple metric of robustness, i.e., the variances of the network phase relationships, and show that robustness is indeed restored simultaneously along with its stable network activity, indicating that, whatever the reorganization of the network entails, it is deep enough also to restore this important property.
神经元活动的稳健性是神经网络抵御扰动所必需的特性，否则这些扰动可能会破坏或摧毁该系统。已表明复杂系统的稳健性取决于系统的许多特征，包括组成神经元活动的形态和异质性、网络大小、突触连接性和神经调节。尽管一些因素与复杂系统的预期特性不一致，例如尺寸小和突触连接的同质性，但小型网络（如甲壳类动物口胃神经系统的幽门网络）的活动似乎具有稳健性。幽门网络的活动已被证明以神经调节状态依赖的方式稳定且稳健。当切断神经调节输入时，活动最初会受到干扰，失去稳定性和稳健性。然而，从长期来看，即使没有恢复神经调节输入，稳定的活动也会通过稳态恢复。我们在本研究中探讨的问题是，随着网络自我重组以补偿神经调节输入的丧失并恢复丧失的活动，稳健性是否也能恢复。在这里，我们使用温度变化作为一种扰动来探究网络活动的稳健性。我们开发了一种简单的稳健性度量指标，即网络相位关系的方差，并表明稳健性确实与稳定的网络活动同时恢复，这表明，无论网络的重组涉及什么，其深度足以恢复这一重要特性。
