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.
Highlights
Memory experiments demonstrated persistent activity in several structures in the lower mammal, primate, and human brains including the hippocampus [1,2], prefrontal [3], visual [4] and oculomotor cortex [5], basal ganglia [6], etc
For the first time in neural memory models, we have demonstrated a method allowing input dimension not to be constrained to a fixed size or be a priori bounded; dimension can change with time, similar to organic memory allocation for memories of greater importance or increased detail
2.1 Our Kernel Heteroassociative Memory Model A general framework of heteroassociative memory can be defined on the input Ex and output Ey spaces, with dimensionalities n and p respectively, and with m pairs of vectors xi[Ex, yi[Ey, i~1 . . . m to be stored
Summary
Memory experiments demonstrated persistent activity in several structures in the lower mammal, primate, and human brains including the hippocampus [1,2], prefrontal [3], visual [4] and oculomotor cortex [5], basal ganglia [6], etc. We introduce a memory model, in which its memory attractors do not lie in the input or neural space as in classical models but rather in a feature space with large or infinite dimension. For the first time in neural memory models, we have demonstrated a method allowing input dimension not to be constrained to a fixed size or be a priori bounded; dimension can change with time, similar to organic memory allocation for memories of greater importance or increased detail. These attributes may be very beneficial in psychological modeling
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