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

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http://arxiv.org/abs/q-bio.NC/0609006

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

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.

It’s quite a long (but mainly informal) paper, I’ll only really be interested in discussing the first 28 pages (not the Sudoku section).

This talk is part of the Machine Learning Journal Club series.

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