title: A high-capacity model for one shot association learning in the brain.
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.
authors.affiliation: Department of Computer Science, Institute of Theoretical Computer Science, ETH Zürich Zürich, Switzerland.
authors.forename: Hafsteinn
authors.initials: H
authors.lastname: Einarsson
authors.affiliation: Department of Computer Science, Institute of Theoretical Computer Science, ETH Zürich Zürich, Switzerland.
authors.forename: Johannes
authors.initials: J
authors.lastname: Lengler
authors.affiliation: Department of Computer Science, Institute of Theoretical Computer Science, ETH Zürich Zürich, Switzerland ; Collegium Helveticum Zürich, Switzerland.
authors.forename: Angelika
authors.initials: A
authors.lastname: Steger
keywords: bootstrap percolation; hetero-associative memory; iterative retrieval; memory capacity; one shot learning; relation learning; stochastic Hebbian learning