ELUCIDATING CENTRAL PATTERN GENERATORS

中枢模式发生器驱动有节奏的行为，如行走， 咀嚼和飞行。 虽然他们已经深入研究， 根本的机制还没有被很好地理解。 长期 这项研究的目的是阐明神经机制， 中央模式发生器的行为，特别是龙虾， 口胃神经节（STG）。 我们的方法是双重的：我们将补充 实验研究与建模。 目前，有无数的细胞生理学参数， 电路行为的模型（Selverston，1988）。 通过 建立STG胃磨部分的简化模型，我们 目标是确定需要考虑哪些生理参数 而这些都与解释无关 我 一种方法是从一个最小集合开始，尽量考虑正常的 振荡行为，并添加属性，因为他们似乎需要 解释更多的行为。 建模工作还提出了实验 需要回答的问题，以告知，约束和验证 模型 建模目标是：1）扩展我们当前的模型， 限制：我们现在可以将间隙连接，延迟和内在 模型中的电流。 我们将使用实际数据来训练模型 人为产生的正弦波数据，以进一步限制它。2） 探索模型预测细胞特性的能力， 细胞已知。 这在其它领域中称为系统识别 任务 如果成功，这可能是非常有用的技术， 识别实验上无法触及的电路组件。 第三章 调查关于功能限制的假设 对电路形式的限制。 4)研究动脉瘤性 权重和相位之间的关系。 5)研究分析方法 例如分叉理论和弛豫振荡器模型， 了解我们模型的数学方面。 这些建模实验需要比目前可用的更多的数据。 按计划进行以下生物学实验：1）获得更好的突触 强度和电紧张耦合估计正常和 神经调节条件。 2)更好地估计输入/输出 单个神经元在不同条件下的功能和内在电流 条件3)确定细胞杀伤引起的扰动的影响 和胃磨输出的超极化4)确定 暴露于假定的 神经调质

Central pattern generators drive rhythmic behaviors such as walking, chewing and flying. While they have been intensively studied, the underlying mechanisms are as yet not well understood. The long term objective of this search is to elucidate the neural mechanisms underlying the behavior of central pattern generators, in particular, the lobster stomatogastric ganglion (STG). Our approach is twofold: We will complement experimental investigation with modeling. Currently, there are a myriad of cell physiology parameters that could be incorporated into a model of circuit behavior (Selverston, 1988). By building simplifying models of the gastric mill portion of the STG, our goal is to determine which physiological parameters am necessary to account for the behavior, and which are peripheral to that explanation. My approach is to start with a minimal set, try to account for normal oscillatory behavior, and add properties as they appear necessary for accounting for more behavior. The modeling work also raises experimental questions which need to be answered to inform, constrain and validate the model. The modeling goals are: 1) Extend our current model with further constraints: We now can incorporate gap junctions, delay, and intrinsic currents in the model. We will use actual data to train the model instead of artificially produced sine wave data to further constrain it. 2) Explore the ability of the model to predict cell properties where not every cell is known. This is known in other fields as a systems identification task. If successful this could be extremely useful technique for identifying circuit components that are inaccessible experimentally. 3) Investigate hypotheses regarding constraints concerning functional constraints on the form of circuit. 4) Investigate the relationship between weights and phase relationships. 5) Investigate analytical methods such as bifurcation theory and models of relaxation oscillators to understand the mathematical aspects of our model. These modeling experiments require more data than is currently available. following biological experiments as planned: 1) Obtain better synaptic strength and electrotonic coupling estimates under normal and neuromodulated conditions. 2) Obtain a better estimate of the input/output function and intrinsic currents of a single neuron under different conditions. 3) Determine the effect of perturbations caused by cell kills and hyperpolarization on the output of the gastric mill. 4) Determine the changes m non-spiking oscillations following exposure to putative neuromodulators.