Collaborative Research: High Performance Neural Computing

研究人员建议：a)开发工具，利用具有数千个（异构或同质）处理器的现代计算机，对哺乳动物皮层的大面积进行电生理学上的逼真模拟；b)使用遗传编程技术来进化初级和次级视觉皮层区域的模型，以解决困难的图像处理任务，即图像分割；c)理解大脑执行的计算结构(即，它的计算原语)，并发现这些计算所必需和充分的生物细节水平。所提出的方法的一个显著特征是，生理现实性不是目标，它将只在理解神经计算和解决复杂信息处理任务所需的程度上进行尝试。也就是说，功能性能将是弥合现有知识差距的手段。因此，由此产生的皮质模型介于传统的（和过于简化的）人工神经网络和受生物医学启发的细胞和分子描述之间。

The investigators propose to a) develop tools for electrophysiologically realistic simulations of large areas of mammalian cortex using modern computers with many thousands of (hetero- or homogeneous) processors b) use genetic programming techniques to evolve models of primary and secondary visual cortical areas to solve difficult image processing tasks, namely image segmentation, c)understand the structure of computations performed by the brain (that is, its computational primitives) and discover the level of biological detail necessary and sufficient for these computations.A distinguishing trait of the proposed approach is that physiological realism is not the goal, and it will be attempted only to the extent that it is needed for understanding the neural computation and for solving complex information processing tasks. That is, functional performance will be the means of bridging over gaps in the existing knowledge. Thus the resulting cortical models fall between the traditional (and oversimplified) Artificial Neural Networks and biomedically-inspired cellular and molecular descriptions.