Auditory midbrain coding of statistical learning that results from discontinuous sensory stimulation

by Hugo Cruces-Solís, Zhizi Jing, Olga Babaev, Jonathan Rubin, Burak Gür, Dilja Krueger-Burg, Nicola Strenzke, Livia de Hoz

Detecting regular patterns in the environment, a process known as statistical learning, is essential for survival. Neuronal adaptation is a key mechanism in the detection of patterns that are continuously repeated across short (seconds to minutes) temporal windows. Here, we found in mice that a subcortical structure in the auditory midbrain was sensitive to patterns that were repeated discontinuously, in a temporally sparse manner, across windows of minutes to hours. Using a combination of behavioral, electrophysiological, and molecular approaches, we found changes in neuronal response gain that varied in mechanism with the degree of sound predictability and resulted in changes in frequency coding. Analysis of population activity (structural tuning) revealed an increase in frequency classification accuracy in the context of increased overlap in responses across frequencies. The increase in accuracy and overlap was paralleled at the behavioral level in an increase in generalization in the absence of diminished discrimination. Gain modulation was accompanied by changes in gene and protein expression, indicative of long-term plasticity. Physiological changes were largely independent of corticofugal feedback, and no changes were seen in upstream cochlear nucleus responses, suggesting a key role of the auditory midbrain in sensory gating. Subsequent behavior demonstrated learning of predictable and random patterns and their importance in auditory conditioning. Using longer timescales than previously explored, the combined data show that the auditory midbrain codes statistical learning of temporally sparse patterns, a process that is critical for the detection of relevant stimuli in the constant soundscape that the animal navigates through.

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