Statistical Models of the Primate Neocortex: Implementation and Application

灵长类动物新皮质的统计模型：实现和应用

• 批准号: 0534858
• 负责人: Thomas Dean
• 金额: $ 48万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-11-15 至 2009-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0534858&HistoricalAwards=false
• 关键词: Statistical; Models; Primate; Neocortex; Implementation

Proposal 0534858"Statistical Models of the Primate Neocortex: Implementation and Application"PI: Thomas L. DeanBrown UniversityABSTRACTTheoretical accounts of the primate cerebral cortex (neocortex) are sufficiently rich in their predictive power and detailed in their specification that they warrant a concerted effort to implement and subject to computational experiment. This project applies recent work in theoretical neuroscience to develop statistical models and related learning and inference algorithms that capture the structure, scale and power of the neocortex for applications requiring robust associative recall, sensor fusion, pattern completion and sequence prediction. The cortical models and algorithms are implemented on moderate-sized computing clusters by distributing the computations among a large number of weakly coupled processes, each of which is capable of reproducing the aggregate behavior of a columnar cortical structure consisting of several thousand neurons. These simulated cortical columns are organized hierarchically much as they are in the primate neocortex. In addition to providing insight into the structure and function of the neocortex, the resulting algorithms and statistical models will enable researchers to combine lessons learned from biology with state-of-the-art graphical-model and machine-learning techniques to design hybrid systems that combine the best of biological and traditional computing approaches.

提案 0534858“灵长类新皮质的统计模型：实施和应用”PI：托马斯·L·迪安·布朗大学摘要灵长类大脑皮层（新皮质）的理论描述具有足够丰富的预测能力，并且在其规范中详细说明，因此它们需要共同努力来实施并进行计算实验。该项目应用理论神经科学的最新成果来开发统计模型以及相关的学习和推理算法，以捕获新皮质的结构、规模和能力，以用于需要强大的联想回忆、传感器融合、模式完成和序列预测的应用。皮质模型和算法是在中等规模的计算集群上实现的，通过将计算分布在大量弱耦合过程中，每个弱耦合过程都能够再现由数千个神经元组成的柱状皮质结构的聚合行为。这些模拟的皮质柱是按层次结构组织的，就像它们在灵长类新皮质中一样。除了深入了解新皮质的结构和功能之外，由此产生的算法和统计模型将使研究人员能够将从生物学中学到的经验教训与最先进的图形模型和机器学习技术相结合，以设计结合了最好的生物学和传统计算方法的混合系统。