AUTHOR=Einarsson Hafsteinn , Lengler Johannes , Steger Angelika TITLE=A high-capacity model for one shot association learning in the brain JOURNAL=Frontiers in Computational Neuroscience VOLUME=8 YEAR=2014 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2014.00140 DOI=10.3389/fncom.2014.00140 ISSN=1662-5188 ABSTRACT=

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs.