MODELING A MOTOR PATTERN GENERATING NEURONAL NETWORK

我们已经详细分析了产生心跳的神经网络 在水蛭里。心脏中间神经元形成相互抑制的对 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一个8个细胞的定时振荡器网络 心跳。还有其他中间神经元，以及振荡器 中间神经元，抑制心脏运动神经元，将它们的活动塑造成 有节奏的爆发声。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运动前中间神经元产生两个 运动神经元的交替协调状态。这一时期的 节律产生的中间神经元受内源性RFamide的调节 神经肽。 我们已经探索了离子流，并分级和尖峰调节 促进振荡器振荡的突触传递 并将这些数据合并到基于电导的 计算机模型。该模型具有相当大的预测价值， 导致了对相互抑制神经元如何产生 振荡性。我们现在处于有利地位，可以向上扩展这一模式， 要水平地涵盖整个心跳网络，以阐明 FMRFamide的调节机制，以及向下，整合细胞 形态学。这些建模研究与并行 生理实验，这里提议的或已经在进行中的 该实验室将有助于我们理解节律性运动是如何起作用的 生成、分段间协调、调制和重新配置在 网络、细胞、离子电流和突触水平。 通过研究水蚤运动模式的形成过程，我们将 揭示对更复杂的马达功能的重要见解 系统。我们提出的计算方法也将产生 对分布式神经网络功能的总体洞察。

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.