Flexible Kernel Memory

Dimitri Nowicki and Hava Siegelmann

PLOS ONE, 2010, vol. 5, issue 6, 1-18

Abstract: This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0010955

DOI: 10.1371/journal.pone.0010955

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