Localist attractor networks
Attractor networks, which map an input space to a discrete
output space, are useful for pattern completion -- cleaning up
noisy or missing input features. However, designing a net to
have a given set of attractors is notoriously tricky; training
procedures are CPU intensive and often produce spurious attractors and
ill-conditioned attractor basins. These difficulties occur because
each connection in the network participates in the encoding of
multiple attractors. We describe an alternative formulation of
attractor networks in which the encoding of knowledge is local, not
distributed. Although localist attractor networks have similar dynamics
to their distributed counterparts, they are much easier to work with
and interpret. We propose a statistical formulation of localist
attractor net dynamics, which yields a convergence proof and a
mathematical interpretation of model parameters. We present
simulation experiments that explore the behavior of localist attractor
networks, showing that spurious attractors are rare, and they facilitate two
desireable properties of psychological and neurobiological models,
priming -- faster convergence to an attractor if the attractor
has been recently visited -- and gang effects -- in which the presence
of an attractor enhances the attractor basins of neighboring attractors.
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