Modeling Reconsolidation in Kernel Associative Memory |
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| Authors: | Dimitri Nowicki Patrick Verga Hava Siegelmann |
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| Institution: | 1. The Biologically Inspired Neural and Dynamic Systems (BINDS) Lab, Department of Computer Science, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America.; 2. Institute of Mathematical Machines & Systems Problems of NASU, Center for Cybernetics, Kiev, Ukraine.; 3. Program of Neuroscience and Behavior, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America.; McGill University, Canada, |
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| Abstract: | Memory reconsolidation is a central process enabling adaptive memory and the perception of a constantly changing reality. It causes memories to be strengthened, weakened or changed following their recall. A computational model of memory reconsolidation is presented. Unlike Hopfield-type memory models, our model introduces an unbounded number of attractors that are updatable and can process real-valued, large, realistic stimuli. Our model replicates three characteristic effects of the reconsolidation process on human memory: increased association, extinction of fear memories, and the ability to track and follow gradually changing objects. In addition to this behavioral validation, a continuous time version of the reconsolidation model is introduced. This version extends average rate dynamic models of brain circuits exhibiting persistent activity to include adaptivity and an unbounded number of attractors. |
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