Latent attractor selection in the presence of irrelevant stimuli

Latent attractor networks are recurrent neural networks with weak attractors that bias the network´s response to external stimuli but never fully manifest themselves. Such networks have been used to model context-dependent place representations in the hippocampus, and to encode context-dependent stimuli in neural networks. In the original latent attractor model, each attractor was triggered by a unique context pattern representing a stimulus that uniquely identified the context of the subsequent episode. 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. In this paper, we describe a network model that can select contexts even if the triggering stimulus patterns are interspersed among patterns irrelevant to context selection. This is closer to the way such a process would occur cognitively, where contexts are typically recognized based on a subset of sequentially perceived identifiers or cues among a larger set of perceived items