Statistical Models of the Primate Neocortex: Implementation and Application

提案0534858“灵长类动物大脑皮层的统计模型：实现和应用”PI: Thomas L. DeanBrown university . abstract灵长类动物大脑皮层（新皮层）的理论描述在其预测能力方面是足够丰富的，并且在其规范中有详细的说明，它们保证了协同努力的实现和服从于计算实验。该项目应用理论神经科学的最新研究成果，开发统计模型和相关的学习和推理算法，捕捉新皮层的结构、规模和能力，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进程中，每个进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。这些模拟的皮层柱的组织层次与灵长类动物的新皮层非常相似。除了提供对新皮层结构和功能的深入了解之外，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吸取的经验教训与最先进的图形模型和机器学习技术结合起来，设计出结合生物和传统计算方法的混合系统。

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.