标题： 搜索记忆，数独，隐式校验位，以及非始终正确的快速神经计算的迭代使用 显示英文标题

标题： Searching for memories, Sudoku, implicit check-bits, and the iterative use of not-always-correct rapid neural computation

摘要： 简单反馈神经回路所代表的大脑区域能够快速执行的算法通常只能解决简单的问题，而对于更困难的问题可能会返回错误的答案。 一种新的兴奋-抑制关联记忆电路模型表现出人类常见的问题，即当只有少量线索时难以快速找到记忆。 该记忆模型和一个用于解决数独谜题的相关计算网络所产生的答案，在信息表示中包含隐式的校验位，使得通过与视觉“突出”相关的计算可以快速评估假设答案是否正确或错误。 这一事实可能解释了我们在从最小线索中检索名义记忆时产生的强烈心理上的对或错的感觉。 这些信息允许通过多次使用计算模块的快速但有限的能力，以串行方式完成更复杂的计算或记忆检索。 关于兴奋-抑制电路在关联记忆和数独中的数学原理，两者都通过“能量”或李雅普诺夫函数来理解，已进行了详细描述。 显示英文摘要

摘要： The algorithms that simple feedback neural circuits representing a brain area can rapidly carry out are often adequate to solve only easy problems, and for more difficult problems can return incorrect answers. A new excitatory-inhibitory circuit model of associative memory displays the common human problem of failing to rapidly find a memory when only a small clue is present. The memory model and a related computational network for solving Sudoku puzzles produce answers that contain implicit check-bits in the representation of information across neurons, allowing a rapid evaluation of whether the putative answer is correct or incorrect through a computation related to visual 'pop-out'. This fact may account for our strong psychological feeling of right or wrong when we retrieve a nominal memory from a minimal clue. This information allows more difficult computations or memory retrievals to be done in a serial fashion by using the fast but limited capabilities of a computational module multiple times. The mathematics of the excitatory-inhibitory circuits for associative memory and for Sudoku, both of which are understood in terms of 'energy' or Lyapunov functions, is described in detail.