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Authors & Affiliations
Sara Jamali,Brice Bathellier,Stanislas Dehaene,Timo van Kerkoerle
Abstract
The ability to extract temporal regularities at different time scales in sensory inputs and detect unexpected deviations from these regularities is a key cognitive ability. The classical auditory oddball paradigm shows that the brain responds to sequence violations at a local time scale, but such responses also occur under anesthesia and therefore seem pre-attentive. In contrast, recent studies in humans and monkeys suggest that when the violation concerns regularities occurring over longer time scales, responses to the violation appear only in conscious, attentive subjects. To investigate whether local and global sequence violation responses exist in the mouse, we recorded from layer 1 to 5 of the auditory cortex using two-photon calcium imaging while mice passively listened to repetitions of 1s-long sequences of five tones. The repeated short sequence contained either a single tone (AAAAA) or a local violation at its end (AAAAB). Purely global violations could be generated by presenting occasionally the AAAAA sequence in a block where AAAAB is repeated. We found that a population of neurons in the auditory cortex specifically responds to such purely global violations at the end of the AAAAA sequence. Although small, this population contained enough information to predict violations on single trials. A larger fraction of neurons boosted their responses to combinations of local and global violations (AAAAB presented in an AAAAA block). These global responses were resistant to a wide increase of inter-sequence interval (1.5s - 25s) ruling out that short-term adaptation causes these responses. However, global responses vanished when the difference between A and B sounds is less salient to the mouse. These results establish that the mouse brain is able to detect global violations in sound sequences in a subgroup of auditory cortex neurons, paving the way for the study of circuit mechanisms underlying long-term temporal regularity detection.