We revisit the planning problem in the blocks world, and we implement a known heuristic for this task. Importantly, our implementation is biologically plausible, in the sense that it is carried out exclusively through the spiking of neurons. Even though much has been accomplished in the blocks world over the past five decades, we believe that this is the first algorithm of its kind. The input is a sequence of symbols encoding an initial set of block stacks as well as a target set, and the output is a sequence of motion commands such as ``put the top block in stack 1 on the table''. The program is written in the Assembly Calculus, a recently proposed computational framework meant to model computation in the brain by bridging the gap between neural activity and cognitive function. Its elementary objects are assemblies of neurons (stable sets of neurons whose simultaneous firing signifies that the subject is thinking of an object, concept, word, etc.), its commands include project and merge, and its execution model is based on widely accepted tenets of neuroscience. A program in this framework essentially sets up a dynamical system of neurons and synapses that eventually, with high probability, accomplishes the task. The purpose of this work is to establish empirically that reasonably large programs in the Assembly Calculus can execute correctly and reliably; and that rather realistic -- if idealized -- higher cognitive functions, such as planning in the blocks world, can be implemented successfully by such programs.

翻译：我们重新审视了街区世界的规划问题, 我们执行了一个已知的螺旋性任务。 重要的是, 我们的执行在生物学上是可信的, 因为它完全通过神经元的跳跃执行。 尽管在过去五十年中, 在街区世界里已经取得了许多成就, 但我们相信这是第一个这样的算法。 输入是一系列符号的序列, 编码了一组最初的块堆以及一个目标集, 输出是一系列动作命令的序列, 比如“ 将桌面上第1堆的顶层块 ” 。 这个框架中的一个程序基本上设置了一个神经元和顶部的动态系统, 最近提出的计算框架, 目的是通过弥合神经活性和认知功能之间的差距来模拟大脑的计算。 它的基本目标就是神经元( 一组神经元, 这些神经元的同步发射信号表明, 主题正在思考一个物体、 概念、 单词, 以及一个目标, 包括项目和合并, 其执行模式可以建立在广泛接受的神经科学信条之上。 这个框架中的一个程序基本上设置一个神经元和顶部的动态系统, 最近提出的计算框架, 能够顺利地执行这个伟大的程序, 。