Flexible Kernel Memory

By Dimitri Nowicki, Hava T. Siegelmann. 2010

21 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition

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Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, background): “Localist attractor networks (Zemel and Mozer, 2001) and flexible kernel memory (Nowicki and Siegelmann, 2010) are two approaches that address this issue by introducing a separate representation for each attractor.”