  Current state-of-the-art object-centric models use slots and attention-based
routing for binding. However, this class of models has several conceptual
limitations: the number of slots is hardwired; all slots have equal capacity;
training has high computational cost; there are no object-level relational
factors within slots. Synchrony-based models in principle can address these
limitations by using complex-valued activations which store binding information
in their phase components. However, working examples of such synchrony-based
models have been developed only very recently, and are still limited to toy
grayscale datasets and simultaneous storage of less than three objects in
practice. Here we introduce architectural modifications and a novel contrastive
learning method that greatly improve the state-of-the-art synchrony-based
model. For the first time, we obtain a class of synchrony-based models capable
of discovering objects in an unsupervised manner in multi-object color datasets
and simultaneously representing more than three objects.
