Data from: Slow cortical dynamics generate context processing and novelty detection

The cortex amplifies responses to novel stimuli, compared to those
elicited by redundant stimuli—a function key to efficiently processing
sensory information and building predictive models of the environment.
Novelty detection is measured by the “Mismatch Negativity” (MMN) signal,
the reduction of which represents the best functional biomarker of
schizophrenia. To better understand the circuit mechanisms of novelty
detection, we used an auditory “oddball” paradigm and two-photon calcium
imaging to measure responses to simple and complex stimuli in neuronal
populations across the mouse auditory cortex. Stimulus statistics and
complexity generated differences in neural response profiles across
contexts and auditory cortical subregions. At the population level,
neuronal ensembles separately and reliably encoded basic auditory
features, as well as temporal context. Interestingly, stimuli-evoked
responses were particularly long-lasting, persisting after the stimuli
ended and affecting responses to future stimuli. These slow network
dynamics encoded stimulus history and temporal context, generating novelty
detection. Recurrent neural network models trained on the oddball task
exhibited slow network dynamics and recapitulated the biological data,
including context selectivity, MMN, and stimulus-specific adaptation. We
conclude that the slow dynamics of recurrent cortical networks underlies
temporal processing of stimuli, a canonical computation that gives rise to
context-specific encoding and novelty detection.