Volen Center and Biology Department, Brandeis University, Waltham, United States.
Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, United States.
Elife. 2022 Mar 18;11:e76579. doi: 10.7554/eLife.76579.
Neural circuits can generate many spike patterns, but only some are functional. The study of how circuits generate and maintain functional dynamics is hindered by a poverty of description of circuit dynamics across functional and dysfunctional states. For example, although the regular oscillation of a central pattern generator is well characterized by its frequency and the phase relationships between its neurons, these metrics are ineffective descriptors of the irregular and aperiodic dynamics that circuits can generate under perturbation or in disease states. By recording the circuit dynamics of the well-studied pyloric circuit in , we used statistical features of spike times from neurons in the circuit to visualize the spike patterns generated by this circuit under a variety of conditions. This approach captures both the variability of functional rhythms and the diversity of atypical dynamics in a single map. Clusters in the map identify qualitatively different spike patterns hinting at different dynamic states in the circuit. State probability and the statistics of the transitions between states varied with environmental perturbations, removal of descending neuromodulatory inputs, and the addition of exogenous neuromodulators. This analysis reveals strong mechanistically interpretable links between complex changes in the collective behavior of a neural circuit and specific experimental manipulations, and can constrain hypotheses of how circuits generate functional dynamics despite variability in circuit architecture and environmental perturbations.
神经回路可以产生多种尖峰模式,但只有部分是功能性的。研究回路如何产生和维持功能性动力学的工作受到了阻碍,因为缺乏对功能性和非功能性状态下回路动力学的描述。例如,尽管中枢模式发生器的规则振荡可以通过其频率和神经元之间的相位关系来很好地描述,但是这些指标对于在扰动或疾病状态下电路可以产生的不规则和非周期性动力学来说并不是有效的描述符。通过记录研究充分的幽门电路的回路动力学,我们使用来自电路中神经元的尖峰时间的统计特征来可视化该电路在各种条件下产生的尖峰模式。这种方法既可以捕捉到功能节律的可变性,也可以捕捉到单个图谱中异常动力学的多样性。图谱中的聚类可以识别出不同的尖峰模式,暗示了电路中的不同动态状态。状态概率和状态之间转换的统计数据随环境扰动、下行神经调质输入的去除以及外源性神经调质的添加而变化。这种分析揭示了神经回路的集体行为的复杂变化与特定实验操作之间具有很强的机制可解释的联系,并且可以限制关于电路如何产生功能动力学的假设,尽管电路结构和环境扰动存在变异性。