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The soundscape consists of a cacophony of multiple sources of sounds with complex properties overlapping temporally and spectrally.  Nonetheless, what we can hear is an orderly acoustic stream organised according to sources and auditory objects, allowing us to distinguish deviant or novel events and select some sources or objects for further processing. Recent evidence suggests that these perceptual achievements are based on properties that are encoded at earlier stages of the auditory pathway. Stimulus-specific adaptation (SSA) is the reduction in the responses to a common sound relative to the same sound when rare. It was originally described in the primary auditory cortex (A1) as the neuronal correlate of the mismatch negativity (MMN), an important component of the auditory event-related potentials that is elicited by changes in the auditory environment. However, the relationship between SSA and the MMN is still a subject of debate. The MMN is a mid-late potential (~150-200 ms in humans), and its neural sources have been located mainly within non-primary auditory cortex in humans and animal models. Moreover, SSA is also present as early as in the auditory midbrain and thalamus (IC and MGB). In this talk, I will show our recent findings on recordings from single neurons in the IC, MGB and auditory cortex (AC) of anaesthetized rats and awake mouse to an oddball paradigm similar to that used for MMN studies. Our data demonstrate that: 1) Most neurons in the non-lemnical divisions of the auditory brain show strong SSA; 2) SSA varies within the neuronal receptive field. 3) GABAergic and/or glycinergic inhibition play a role in modulating SSA in the IC and MGB.; but it seems that Acetylcholine shapes SSA by differently affecting the response to the standard sounds only. 4) Our most recent recordings from different AC fields demonstrate that SSA is much stronger and develops faster in non-primary than in primary auditory cortex, paralleling the organization of subcortical SSA. And finally, 5) we unravel the hierarchical emergence of prediction error signals along the central auditory system. These error signals are detectable already at subcortical levels and correlated with large-scale mismatch responses in auditory cortex. Thus we demonstrate that deviance detection can be tracked down to the neuronal level and highlight the role of subcortical structures in cognition. Taken together our results unify three coexisting views of perceptual deviance detection at different levels of description: neuronal physiology, cognitive neuroscience and the theoretical predictive coding framework.

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