= Statistical separation of objects in clutter
Independently varying companions can make an individual object’s features more consistently related than the features of any particular multi-object conjunction. Competitive networks can exploit this difference to learn separate object representations without being shown each object in isolation. The result depends on the training distribution; a fixed co-occurring group can instead be learned as a conjunction.<Invariant object recognition with trace learning and multiple stimuli present during training by Simon M. Stringer and others>[Abstract; Network 18, pp. 161–187, changing object repertoire and <trace learning> with multiple objects]{ref}<Learning transform invariant object recognition in the visual system with multiple stimuli present during training by Simon M. Stringer and others>[Abstract; <Neural networks> 21, pp. 888–903, competitive networks and rotating 3D objects]{ref}
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