title: Flexible kernel memory.
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.
authors.affiliation: Biologically Inspired Neural and Dynamical Systems (BINDS) Lab, Department of Computer Science, University of Massachusetts Amherst, Amherst, Massachusetts, USA. nowicki@cs.umass.edu
authors.forename: Dimitri
authors.initials: D
authors.lastname: Nowicki
authors.forename: Hava
authors.initials: H
authors.lastname: Siegelmann
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008962:Models, Theoretical; D009473:Neuronal Plasticity