Pattern Recognition Models for Bioinspired Computing and Document Analysis

用于仿生计算和文档分析的模式识别模型

• 批准号: RGPIN-2014-04228
• 负责人: Blostein, Dorothea
• 金额: $ 1.46万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587682
• 关键词: Pattern; Recognition; Models; Bioinspired; Computing

We propose a novel form of bioinspired computing that models the structural adaptability of biological systems. This research addresses the problem that the network properties of fascia (connective tissue) are poorly understood. Our computational model of the structural responsiveness of a biological system leads to advances within computer science, and also contributes insights relevant to applications in medicine, physiology and biological modeling. Our novel form of bioinspired computing is called Fascial Network Computing by analogy with Neural Network Computing, a well-established form of bioinspired computing that models the neural adaptability of biological systems. Fascia is a bodywide network of connective tissue that provides structural support, protection, shock absorption and elastic recoil. Fascial tissue adapts by changing its characteristics in response to the demands placed on it. This inspires our training algorithm for a simulated fascial network: sections of fascia that are frequently under high load respond by increasing their stiffness. Analogously, during training of a neural network, neurons that are frequently co-activated respond by increasing the strength of the connection between them. The pattern of stiffness in a trained fascial network constitutes a type of distributed memory, analogous to the distributed memory formed by the pattern of connection strengths in a trained neural network. Both neural and fascial networks exhibit emergent properties such as global response to local injury. Fascial Network Computing offers insight into the structural responsiveness of a biological system, an intriguing complement to the neural responsiveness modeled by Neural Network Computing. The first long-term goal is to define Fascial Network Computing as adaptive tensegrity, and investigate its computational properties to advance the state of the art in bioinspired computing. The second long-term goal is to extend Fascial Network Computing to include an abstract model of injury, thereby producing knowledge that increases the accuracy of computer-based concussion models. The requested funding will support the training of two PhD, five MSc and four undergraduate students in leading edge bioinspired computing research. I will train students in technical skills by drawing on my wide range of experience with topics such as pattern recognition, document analysis, abstraction in modeling, model validation, classifier combination, biomedical document retrieval and holographic reduced representations. In addition, by placing emphasis on fostering enthusiasm, confidence, discipline, information acquisition and technical communication in my students, I develop a well-balanced foundation for their success in research.

我们提出了一种新形式的生物启发计算模型的生物系统的结构适应性。这项研究解决了筋膜（结缔组织）的网络特性知之甚少的问题。我们的生物系统的结构响应性的计算模型导致计算机科学的进步，也有助于在医学，生理学和生物建模的应用相关的见解。 我们的生物启发计算的新形式被称为筋膜网络计算，这与神经网络计算类似，神经网络计算是一种成熟的生物启发计算形式，它模拟了生物系统的神经适应性。筋膜是一个全身性的结缔组织网络，提供结构支撑、保护、减震和弹性反冲。筋膜组织通过改变其特性来适应对它的要求。这启发了我们模拟筋膜网络的训练算法：经常处于高负荷下的筋膜部分通过增加其刚度来响应。类似地，在神经网络的训练过程中，经常被共同激活的神经元通过增加它们之间的连接强度来做出反应。 在训练的筋膜网络中的刚度模式构成了一种类型的分布式记忆，类似于在训练的神经网络中由连接强度模式形成的分布式记忆。神经网络和筋膜网络都表现出对局部损伤的整体反应等紧急性质。筋膜网络计算提供了对生物系统的结构响应的深入了解，这是对神经网络计算建模的神经响应的有趣补充。 第一个长期目标是将筋膜网络计算定义为自适应张拉整体，并研究其计算特性，以推动生物启发计算的发展。第二个长期目标是扩展筋膜网络计算，以包括抽象的损伤模型，从而产生知识，提高基于计算机的脑震荡模型的准确性。 申请的资金将支持在前沿生物启发计算研究方面培训两名博士，五名硕士和四名本科生。我将通过利用我在模式识别，文档分析，建模抽象，模型验证，分类器组合，生物医学文档检索和全息简化表示等主题方面的广泛经验来培训学生的技术技能。此外，通过强调培养学生的热情，信心，纪律，信息获取和技术交流，我为他们在研究中取得成功奠定了良好的基础。