Collaborative Research: Spatially Distributed Computation in a Small Neural Network

9604505 很长一段时间以来，神经生物学家一直认为神经细胞是神经系统的基本功能单位。 在这种观点中，细胞（或“神经元”）从选定的来源（感觉细胞，其他神经细胞，血液中的激素）接收输入，处理包含在所有这些输入中的信息，并向其他神经元或肌肉发出信号，代表处理的统一结果。 然而，越来越明显的是，单个神经元的这种功能统一性在某些情况下，也许在许多情况下，是不正确的。 相反，一个小区的不同部分可以各自基于对由整个小区接收的输入的所选择的“本地”采样的处理来发送出其自己的信号。 这项研究将评估这种“局部”计算在细胞中从一个小的神经网络的可能性。 该网络特别适合这项研究，因为它的神经元数量足够少，可以从生理上监测所有向特定目标神经元输入的细胞的活动。 经验一再表明，在这个简单的模型系统（一个被称为“口胃神经节”的螃蟹神经节）中更容易学到的原理可以应用于更复杂的系统，包括哺乳动物和人类的神经系统。单个神经元将被注入荧光染料，使它们在“共聚焦”显微镜下可见。 共聚焦显微镜将记录来自每个细胞所拥有的大量分支延伸的荧光，从而允许精确重建细胞的复杂形状。 由此，以及在注入荧光染料的同时进行的一些简单的电学测量，将构建一个预测电压信号在细胞不同部分的传播的计算机模型。 如果在单元中的任何一个点处生成的信号在行进到单元的其他部分时变化相对较小，则这将证明在单元中存在“本地”计算的可能性很小，因为所有部分将接收相同的处理信号。 如果像初步数据显示的那样，信号在从细胞中的一点传递到另一点时会发生显著变化，那么局部计算的可能性就大得多。 大部分研究集中在本地计算能力对各种生理因素的敏感程度上。 例如，当来自其他神经细胞的输入变得更强时，目标细胞的电特性会发生变化，因此它更有可能分解为小的局部计算区域。 另一方面，如果来自其他细胞的输入广泛分布在目标神经元的表面上，则目标细胞可以以更统一的方式起作用。 该项目正在研究这些不同的参数如何影响真实的神经元的计算风格，以及单个神经元的计算风格如何随着神经系统活动状态的变化而变化。

9604505 Hartline For a long time, neurobiologists have viewed the nerve cell as being the fundamental unit of function in the nervous system. In this view, a cell (or "neuron") receives inputs from selected sources (sensory cells, other nerve cells, hormones in the blood), processes the information contained in the totality of these inputs and sends out a signal to other neurons or to muscles that represent a unified result of the processing. However, it is becoming increasingly apparent that this unity of function of the single neuron may in some cases, and perhaps in many, be incorrect. Instead, different parts of one cell may each be sending out its own signal based on processing of a selected "local" sampling of inputs received by the whole cell. This research will assess the possibilities for such "local" computation in cells from a small neural network. This network is particularly suited to the research because it has few enough neurons that activity from all cells having input onto a particular target neuron can be monitored physiologically. Experience has shown repeatedly that principles learned more easily in this simple model system (a ganglion from a crab termed the "stomatogastric ganglion"), can be applied to more complex systems, including the nervous system of mammals and man. Single neurons will be injected with a fluorescent dye that makes them visible in a "confocal" microscope. The confocal microscope will record the fluorescence coming from the multitude of branching extensions possessed by each cell and thus will allow accurate reconstruction of the complex shape of the cell. From this, and from some simple electrical measurements that can be made at the same time the fluorescent dye is injected, a computer model will be constructed of the predicted spread of voltage signals through the different parts of the cell. If the signals generated at any one point in the cell are changed relatively little in traveling to other parts of the cell, this will be evidence that there is little likelihood of "local" computation in the cell, since all parts will receive the same processed signal. If, as preliminary data suggest, signals can be significantly altered in passing from one point in a cell to another, the likelihood of local computation is much greater. Much of the research focuses on how sensitive the local computation capabilities are to various physiological factors. For example, as input from other nerve cells becomes stronger, the electrical properties of the target cell change so that it is more likely to break up into small local computational regions. On the other hand, if the input from other cells is broadly distributed over the surface of a target neuron, the target cell may act in a more unified fashion. The project is investigating how such different parameters influence the computational style of real neurons and how computational styles of a single neuron may change as the state of activity of the nervous system changes.