标题： 细胞相位维持在中央模式发生器的幽门模样的机制 显示英文标题

标题： Cellular Mechanisms of Phase Maintenance in a Pyloric Motif of a Central Pattern Generator

摘要： 在许多神经网络中，控制节奏行为的模式在广泛的周期范围内保持不变。 在甲壳类动物的幽门中央模式生成器（CPG）中，恒定的爆发模式在周期的三到五倍范围内得以保持。 我们描述了神经调节如何调整神经元特性，以在周期变化时保持相位关系。 我们开发了一个生物物理模型，实现了简化的幽门网络特征，该特征包含一个爆发神经元和两个通过抑制性突触耦合相互连接的跟随神经元。 我们描述了支持相位维持的细胞机制，并研究了这四种动态不同的幽门CPG集合中这些机制之间的可能协调，这些集合产生三相模式。 钾离子（VK2）半激活电压和超极化激活（Vh）电流的协调变化提供了一组三种机制，用于控制爆发持续时间、爆发间间隔和尖峰延迟。 这些机制由基石分岔决定，这是Shilnikov天空蓝灾难场景之一。 在机制1中，在爆发神经元中，当VK2接近天空蓝灾难分岔时，爆发持续时间增加；而当Vh接近不变环上的鞍点分岔时，爆发间间隔增长。 在机制2中，一个静默神经元对短输入作出单个爆发的反应；当VK2接近周期轨道的鞍点分岔时，爆发持续时间增长。 在机制3中，一个尖峰神经元对短输入作出暂停的反应；当Vh接近静态状态的鞍点分岔时，暂停持续时间增长。 在所有三种机制中，当分岔参数接近其临界值时，测量的量会无限制地增长，这与反平方根定律一致。 显示英文摘要

摘要： In many neural networks, patterns controlling rhythmic behaviors are maintained across a wide range of periods. In the crustacean pyloric central pattern generator (CPG), a constant bursting pattern is preserved over a three-to-fivefold range of periods. We idescribe how neuromodulation could adjust neuronal properties to preserve phase relations as the period changes. We developed a biophysical model implementing a reduced pyloric network motif, which has a bursting neuron and two follower neurons interconnected through inhibitory synaptic coupling. We described cellular mechanisms supporting phase maintenance and investigated possible coordination between these mechanisms in four dynamically distinct ensembles of a pyloric CPG producing a triphasic pattern. The coordinated variation of the voltages of half-activation for potassium (VK2) and hyperpolarization-activated (Vh) currents provides a family of three mechanisms for control of burst duration, interburst interval, and latency to spiking. The mechanisms are determined by the Cornerstone bifurcation, one of the Shilnikov blue sky catastrophe scenarios. In Mechanism 1, in a bursting neuron, the burst duration increases as VK2 nears a blue-sky catastrophe bifurcation, while the interburst interval grows as Vh approaches a saddle-node on an invariant circle bifurcation. In Mechanism 2, a silent neuron responds with a single burst to short input; the burst duration grows as VK2 approaches a saddle-node bifurcation for periodic orbits. In Mechanism 3, a spiking neuron responds with a pause to short input; the pause duration grows as Vh nears a saddle-node bifurcation for stationary states. In all three mechanisms, the measured quantities grow without bound as the bifurcation parameter nears its critical value, consistent with an inverse-square-root law.