MODELING A MOTOR PATTERN GENERATING NEURONAL NETWORK

我们已经详细分析了产生心跳的神经元网络 在水蛭里 心脏中间神经元形成相互抑制对 控制心跳节奏的振荡器 其他心脏中间神经元 协调这些振荡器;这些协调中间神经元与 振荡器中间神经元形成8单元定时振荡器网络， 心跳 还有其他的中间神经元，沿着振荡器 中间神经元，抑制心脏运动神经元，将它们的活动塑造成 有节奏的爆发 关键开关中间神经元之间的接口 振荡器中间神经元和其他前运动中间神经元产生两个 运动神经元的交替协调状态。 的周期 产生节律的中间神经元受到内源性RF酰胺的调节 神经肽 我们已经探索了离子电流，分级和尖峰介导的 促进振荡器振荡的突触传递 interneurons并已将这些数据纳入基于电导的 计算机模型 该模型具有相当大的预测价值， 对抑制性神经元如何产生 振荡 我们现在处于有利地位，可以向上扩展这种模式， 水平地涵盖整个心跳网络，以阐明 FMRFamide的调节机制，向下，以纳入细胞 形态学 这些建模研究与并行 生理学实验，无论是在此提出的或已经在进行中， 实验室，将有助于我们了解如何节奏运动行为 被生成、分段协调、调制和重新配置， 网络、细胞、离子电流和突触水平。 通过研究水蛭运动模式的形成过程，我们将 揭示了更复杂的运动功能的重要见解 系统. 我们提出的计算方法也将产生 深入了解分布式神经网络的功能。

We have analyzed in detail the neuronal network that generates heartbeat in the leech. Reciprocally inhibitory pairs of heart interneurons form oscillators that pace the heartbeat rhythm. Other heart interneurons coordinate these oscillators; these coordinating interneurons with the oscillator interneurons form an 8 cell timing oscillator network for heartbeat. Still other interneurons, along with the oscillator interneurons, inhibit heart motor neurons, sculpting their activity into rhythmic bursts. Critical switch interneurons interface between the oscillator interneurons and the other premotor interneurons to produce two alternating coordination states of the motor neurons. The period of the rhythm generating interneurons are modulated by endogenous RFamide neuropeptides. We have explored the ionic currents, and graded and spike-mediated synaptic transmission that promote oscillation in the oscillator interneurons and have incorporated these data into a conductance based computer model. This model has been of considerable predictive value and has led to new insights into how reciprocally inhibitory neurons produce oscillation. We are now in a strong position to expand this model upward, to encompass the entire heartbeat network, horizontally, to elucidate the mechanisms of FMRFamide modulation, and downward, to incorporate cellular morphology. These modeling studies in conjunction with parallel physiological experiments, either proposed herein or already ongoing in the lab, will contribute to our understanding of how rhythmic motor acts are generated, coordinated intersegmentally, modulated and reconfigured at the network, cellular, ionic current, and synaptic levels. By studying the processes for motor pattern formation in the leech we will uncover important insights into the function of more complex motor systems. The computational approach that we propose will also generate insights into the function of distributed neural networks in general.