Mathematical Sciences: Analysis of Neural Networks: Stable Category Formation In A Complex Input Environment

数学科学：神经网络分析：复杂输入环境中的稳定类别形成

• 批准号: 8611959
• 负责人: Gail Carpenter
• 金额: $ 6.83万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-03-15 至 1990-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8611959&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Analysis; Neural; Networks

CARPENTER The investigator intends to develop and analyze models of neural systems networks. These models will be designed to carry out a self-organizing pattern recognition and category learning. As formal architectures, the models will be translated into machine designs. As neural networks, the systems will be based on psychological and physiological data and will be used to analyze the brain and behavior in the process of pattern recognition and category learning. From a mathematical point of view, these models are based on nonlinear, singular, high dimensional systems of ordinary differential equations. Network design is constrained by the conflicting requirements of stability and adaptability, and by the complexity. Given an arbitrary complicated sequence of inputs, the system must be able to extract stable categories, without a teacher. However, the system must also remain plastic, open to new learning. New network designs will generalize systems which have been shown to stably encode arbitrary lists of binary input patterns in such a way that, after an initial phase, each input directly activates its category representation. The search mechanism is automatically disengaged once the learning is complete. The more general system will encode graded input patterns, and be embedded in network hierarchies adapted to the particular constraints of natural and artificial vision and speech. Research described in this project is highly cross-disciplinary, as it involves the neural networks science, communications, some aspects of robotics and computer vision, neural cognitive processes, and mathematics.

木匠 研究人员打算开发和分析神经系统的模型 网络. 这些模型将被设计为进行自组织 模式识别和类别学习。 作为正式的体系结构， 模型将转化为机器设计。 作为神经网络，这些系统将基于心理和 生理数据，并将用于分析大脑和行为， 模式识别和类别学习的过程。 从 从数学的角度来看，这些模型是基于非线性， 奇异的、高维的常微分方程系统。 网络设计受到以下相互冲突的要求的限制： 稳定性和适应性，以及复杂性。 给定任意 复杂的输入序列，系统必须能够提取 稳定的类别，没有老师。 但是，该系统还必须 保持可塑性，接受新的学习。 新的网络设计将 已被证明可以稳定地编码任意 二进制输入模式的列表，在初始 阶段，每个输入直接激活其类别表示。 的 搜索机制是自动脱离一旦学习是 完成. 更通用的系统将对分级输入模式进行编码， 并且被嵌入到适合于特定的 自然和人工视觉和语音的限制。 该项目中描述的研究是高度跨学科的，因为它 涉及到神经网络科学，通信， 机器人和计算机视觉，神经认知过程， 数学