Parallel Computation and Learning

该项目关注的是将复杂性理论的思想发展和应用到三个不同但相关的计算领域：通用并行计算机的理论可能性正在继续研究中。问题是设计理想化的高级模型，既方便程序员，又能在现实技术中有效地模拟。类脑计算模型的计算能力是并行计算领域的一个重要研究课题。如果对时间和空间资源施加生物学上合理的限制，这种神经模型似乎与其他复杂性模型更加不同。第三，部分支持后一项工作，PI将继续研究可以学习或诱导的极限，如多项式时间可计算性等标准所施加的限制。

The project is concerned with developing and applying ideas from complexity theory to three distinct but related areas of computation: Under continued investigation are the theoretical possibilities for general purpose parallel computers. The problem is to devise idealised high level models that are both convenient for the programmer as well as efficiently simulated in realistic technologies. As a distinct topic in parallel computation the computational capabilities of brainlike models of computation are being studied. Such neural models appear to become more distinct from other complexity models if biologically plausible restrictions on time and space resources are imposed. Thirdly, partly in support of this latter work, the PI will continue to investigate the limits of what can be learned or induced, as imposed by such criteria as polynomial time computability.