Parallel Processing in a Neural Network

神经网络的并行处理 拟议研究的目标是进一步了解 神经系统中的并行处理机制。 研究将集中在前庭眼反射（VOR）， 通过产生眼球旋转来平衡 头部旋转。 VOR由前庭神经核神经元介导 （VNN）。 最近的神经网络模型表明， 眼睛运动命令VNN结果的分布式编码 因为VNN并行操作。 潜在的假设是 这种并行性对VOR功能至关重要。 的主要目的 该项目将通过研究定量地检验这一假设， 损伤不同数目的VNN对VOR的影响。 实验对象将是金鱼，因为它们有一个 发展良好的VOR。 为了生成预测，将删除不同数量的VNN 从一个高度平行和现实的金鱼VOR模型。 最初的预测是VNN病变会降低VOR 增加VOR谐波失真。 这将是 通过比较之前和之后的VOR眼球运动进行实验测试， 在VNN损伤后。 将使用眼速对数据进行量化 功率谱 将对病变大小进行组织学定量。 结果将被纳入更大和更现实的VOR 神经网络模型，将在大规模 并行计算机 //

PARALLEL PROCESSING IN A NEURAL NETWORK The goal of the proposed research is to gain further insight into mechanisms of parallel processing in the nervous system. Research will focus on the vestibulo-ocular reflex (VOR), which stabilizes vision by producing eye rotations that counterbalance head rotations. The VOR is mediated by vestibular nuclei neurons (VNNs). Recent neural network models suggest that the distributed encoding of eye-movement commands VNNs results because VNNs operate in parallel. The underlying assumption is that parallelism is critical to VOR function. The primary aim of the project is to quantitatively test this assumption by studying the effects on VOR of lesioning varying numbers of VNNs. Experimental subjects will be goldfish because they have a suitably well-developed VOR. To generate predictions, varying numbers of VNNs will be removed from a highly parallel and realistic model of the goldfish VOR. The initial prediction is that VNN lesions should degrade VOR function by increasing VOR harmonic distortion. This will be tested experimentally by comparing VOR eye-movements before and after VNN lesions. Data will be quantified using eye-velocity power spectra. Lesion size will be quantified histologically. Results will be incorporated into larger and more realistic VOR neural network models, which will be implemented on massively parallel computers. //