ELUCIDATING CENTRAL PATTERN GENERATORS

中央模式生成器驱动有节奏的行为，如行走， 咀嚼和飞行。虽然对它们进行了深入的研究，但 潜在的机制到目前为止还没有被很好地理解。从长远来看 这项研究的目的是阐明 中央模式生成器的行为，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 用模型进行实验研究。 目前，有无数的细胞生理学参数可以 并入电路行为模型(Selverston，1988)。通过 建立STG的胃磨部分的简化模型，我们的 目标是确定哪些生理参数是必须考虑的 对于行为来说，这对这个解释是无关紧要的。我的 方法是从最小集合开始，尝试考虑正常 振荡行为，并根据需要添加属性 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 模特。 建模目标是：1)进一步扩展我们当前的模型 限制：我们现在可以结合缝隙连接、延迟和固有 模型中的电流。我们将使用实际数据来训练模型 人工产生的正弦波数据，以进一步限制它。2) 探索该模型预测细胞属性的能力，而不是 手机是已知的。这在其他领域称为系统标识 任务。如果成功，这将是一项非常有用的技术 识别在实验中无法接触到的电路元件。3) 调查有关泛函约束的假设 对电路形式的限制。4)调查关系 权重和相位关系之间的关系。5)研究分析方法 如分叉理论和弛豫振子模型 理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。 按计划进行生物学实验：1)获得更好的突触 正常和正常情况下的强度和电紧耦合估计 神经调节状态。2)获得对输入/输出的更好估计 不同条件下单个神经元的功能和本征电流 条件。3)确定细胞死亡引起的扰动的影响 超极化对胃磨输出的影响。4)确定 暴露于假想环境后m个非尖峰振荡的变化 神经调节剂。

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.