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Neural network models for spatial data mining, map production, and cortical direction selectivity.

机译：用于空间数据挖掘，地图制作和皮质方向选择性的神经网络模型。

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A family of ARTMAP neural networks for incremental supervised learning has been developed over the last decade. The Sensor Exploitation Group of MIT Lincoln Laboratory (LL) has incorporated an early version of this network as the recognition engine of a hierarchical system for fusion and data mining of multiple registered geospatial images. The LL system has been successfully fielded, but it is limited to target vs. non-target identifications and does not produce whole maps. This dissertation expands the capabilities of the LL system so that it learns to identify arbitrarily many target classes at once and can thus produce a whole map. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of a consistent procedure and a benchmark testbed has permitted the evaluation of candidate recognition networks as well as pre- and post-processing and feature extraction options. The resulting default ARTMAP network and mapping methodology set a standard for a variety of related mapping problems and application domains.; The second part of the dissertation investigates the development of cortical direction selectivity. The possible role of visual experience and oculomotor behavior in the maturation of cells in the primary visual cortex is studied. The responses of neurons in the thalamus and cortex of the cat are modeled when natural scenes are scanned by several types of eye movements. Inspired by the Hebbian-like synaptic plasticity, which is based upon correlations between cell activations, the second-order statistical structure of thalamo-cortical activity is examined. In the simulations, patterns of neural activity that lead to a correct refinement of cell responses are observed during visual fixation, when small ocular movements occur, but are not observed in the presence of large saccades. Simulations also replicate experiments in which kittens are reared under stroboscopic illumination. The abnormal fixational eye movements of these cats may account for the puzzling finding of a specific loss of cortical direction selectivity but preservation of orientation selectivity. This work indicates that the oculomotor behavior of visual fixation may play an important role in the refinement of cell response selectivity.
机译：在过去的十年中，开发了一系列用于增量式监督学习的ARTMAP神经网络。麻省理工学院林肯实验室（LL）的传感器开发小组已将此网络的早期版本合并为分层系统的识别引擎，用于对多个已注册地理空间图像进行融合和数据挖掘。 LL系统已成功部署，但仅限于目标识别与非目标识别，并且无法生成完整地图。本文扩展了LL系统的功能，使它学会了一次识别任意多个目标类别的能力，从而可以生成完整的地图。这种新的空间数据挖掘系统专门设计用于应对典型映射问题的高度倾斜的类分布。一致的程序和基准测试平台的规范允许评估候选识别网络以及预处理和后处理以及特征提取选项。最终的默认ARTMAP网络和映射方法为各种相关的映射问题和应用程序领域设定了标准。论文的第二部分研究了皮层方向选择性的发展。研究了视觉体验和动眼行为在初级视觉皮层细胞成熟中的可能作用。当通过几种类型的眼睛运动扫描自然场景时，可以模拟猫丘脑和皮层中神经元的反应。受类似于Hebbian的突触可塑性（基于细胞激活之间的相关性）的启发，研究了丘脑皮质活动的二级统计结构。在模拟中，在视觉固定过程中会观察到导致细微眼动运动的神经活动模式，从而导致细胞反应的正确细化，而在大扫视的情况下则观察不到。模拟还复制了在频闪镜照明下饲养小猫的实验。这些猫的异常注视眼运动可能会导致令人困惑的发现，即皮质方向选择性的特定损失，但保留了方向选择性。这项工作表明视觉固定的动眼行为可能在细化细胞反应选择性中起重要作用。

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