Latent attractor selection for variable length episodic context stimuli with distractors

Latent attractor networks have been proposed as a possible mechanism for representing episodic context in the hippocampus, and as general purpose models of episodic context-dependent encoding in neural networks. These are recurrent neural networks with attractors that never fully manifest themselves, but bias the network's response to external stimuli. While each attractor in the original latent attractor model was triggered by unique context patterns specific to the context, this model was later extended to the case where contexts were triggered progressively by the sequential presentation of several stimulus patterns without regard to order, simulating the more realistic situation where a context is identified by a sequentially scanned combination of landmarks. In this paper, we describe a network model that can select among contexts identified by overlapping sequences of different lengths, even if the relevant stimulus patterns are interspersed among patterns irrelevant to context selection.