Liu Jian, Lu Wenbo, Yuan Ye, Xin Kuankuan, Zhao Peng, Gu Xiao, Raza Asif, Huo Hong, Li Zhaoyu, Fang Tao
Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China.
Front Neurosci. 2022 Apr 25;16:808824. doi: 10.3389/fnins.2022.808824. eCollection 2022.
Understanding the structure-function relationship in a neuronal network is one of the major challenges in neuroscience research. Despite increasing researches at circuit connectivity and neural network structure, their structure-based biological interpretability remains unclear. Based on the attractor theory, here we develop an analytical framework that links neural circuit structures and their functions together through fixed point attractor in . In this framework, we successfully established the structural condition for the emergence of multiple fixed points in connectome. Then we construct a finite state machine to explain how functions related to bistable phenomena at the neural activity and behavioral levels are encoded. By applying the proposed framework to the command circuit in , we provide a circuit level interpretation for the forward-reverse switching behaviors. Interestingly, network properties of the command circuit and first layer amphid interneuron circuit can also be inferred from their functions in this framework. Our research indicates the reliability of the fixed point attractor bridging circuit structure and functions, suggesting its potential applicability to more complex neuronal circuits in other species.
理解神经元网络中的结构-功能关系是神经科学研究的主要挑战之一。尽管对电路连通性和神经网络结构的研究不断增加，但其基于结构的生物学可解释性仍不明确。基于吸引子理论，我们在此开发了一个分析框架，该框架通过 中的不动点吸引子将神经电路结构及其功能联系在一起。在这个框架中，我们成功地建立了连接体中出现多个不动点的结构条件。然后我们构建了一个有限状态机来解释与神经活动和行为水平上的双稳态现象相关的功能是如何编码的。通过将所提出的框架应用于 中的命令电路，我们为正向-反向切换行为提供了电路层面的解释。有趣的是，命令电路和第一层双栖中间神经元电路的网络特性也可以从该框架中的功能推断出来。我们的研究表明不动点吸引子在连接电路结构和功能方面的可靠性，表明其在其他物种中对更复杂神经元电路的潜在适用性。
