类脑随机搜索：研究挑战和资助机会
Brain-Like Stochastic Search: A Research Challenge and Funding Opportunity
摘要：
类脑随机搜索（Brain-Like Stochastic Search，BLiSS）就是这样一个任务：给定一个效用函数族U（u，A），其中u是参数或任务描述符的向量，使用网络（Option Nets）输入A并学习随机生成好的选项u，最大化或最小化U。本文讨论了为什么这对类脑智能（NSF资助的一个领域）和许多应用至关重要，并讨论了网络设计和训练的各种可能性。附录讨论了最近的研究，在运筹学中的随机优化工作的关系，和工程为基础的方法来理解新皮层的关系。
英文摘要：
Brain-Like Stochastic Search (BLiSS) refers to this task: given a family of utility functions U(u,A), where u is a vector of parameters or task descriptors, maximize or minimize U with respect to u, using networks (Option Nets) which input A and learn to generate good options u stochastically. This paper discusses why this is crucial to brain-like intelligence (an area funded by NSF) and to many applications, and discusses various possibilities for network design and training. The appendix discusses recent research, relations to work on stochastic optimization in operations research, and relations to engineering-based approaches to understanding neocortex.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
