Spontaneously emerging internal models of visual sequences combine abstract and event-specific information in the prefrontal cortex

When exposed to sensory sequences, do macaque monkeys spontaneously form abstract internal models that generalize to novel experiences? Here, we show that neuronal populations in macaque ventrolateral prefrontal cortex encode visual sequences by factorizing them into separate codes for the specific pictures presented and for their abstract sequential structure. Ventrolateral prefrontal neurons were recorded while macaque monkeys passively viewed visual sequences and sequence mismatches in the local-global paradigm. Even without any overt task or response requirements, prefrontal populations spontaneously built up representations of sequence structure, serial order, and image identity within distinct but superimposed neuronal subspaces. Representations of sequence structure rapidly updated following single exposure to a mismatch sequence, while orthogonal populations represent mismatches for sequences of different complexity. Finally, those representations generalized across sequences following the same structure but comprising different images. These results suggest that prefrontal populations spontaneously encode rich internal models of visual sequences that reflect both content-specific and abstract information.