Searching for memories, Sudoku, implicit check bits, and the iterative use of not-always-correct rapid neural computation.
The algorithms that simple feedback neural circuits representing a brain area can rapidly carry out are often adequate to solve easy problems but 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.
Hopfield
J J
JJ
Carl Icahn Laboratory, Princeton University, Princeton, NJ 08544, USA. hopfield@princeton.edu
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D006801:Humans; D008568:Memory / Q000502:physiology*; D016571:Neural Networks, Computer; D011340:Problem Solving / Q000502:physiology