Collaborative Research: Spatially Distributed Computation in a Small Neural Network

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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.