Parallel Computation and Learning

这项研究涉及两个领域：通用并行计算和神经计算模型。第一个领域将研究在体系结构和语言级别之间找到一个用于并行计算的标准化桥接模型的可能性。这样的标准将隔离软件和硬件的开发，从而加快并行计算行业的成功。块同步并行(BSP)模型将被认为是主要的候选模型。第二个研究领域将进一步开发一种离散的神经计算模型，该模型既足够弱，可以用人类大脑皮层模拟其组成部分，又足够强大，可以在它上面执行一些基本的学习和记忆任务。

The research is in two areas: general purpose parallel computing, and models of neural computation. The first area will investigate the possibility of finding a standardizing bridging model for parallel computing, at a level intermediate between the architecture and language levels. Such a standard would insulate the development of software and hardware and thereby speed the success of the parallel computing industry. The bulk-synchronous parallel (BSP) model will be considered as the prime candidate. The second area of research will further develop a discrete model of neural computation that is both weak enough that its constituents are plausibly simulated by human cortex, yet strong enough that some basic tasks of learning and memory can be implemented on it.