Abstract

Associative learning of pure tones is known to cause tonotopic map expansion in the auditory cortex (ACx), but the function this plasticity sub-serves is unclear. We developed an automated training platform called the “Educage,” which was used to train mice on a go/no-go auditory discrimination task to their perceptual limits, for difficult discriminations among pure tones or natural sounds. Spiking responses of excitatory and inhibitory parvalbumin (PV+) L2/3 neurons in mouse ACx revealed learning-induced overrepresentation of the learned frequencies, as expected from previous literature. The coordinated plasticity of excitatory and inhibitory neurons supports a role for PV+ neurons in homeostatic maintenance of excitation–inhibition balance within the circuit. Using a novel computational model to study auditory tuning curves, we show that overrepresentation of the learned tones does not necessarily improve discrimination performance of the network to these tones. In a separate set of experiments, we trained mice to discriminate among natural sounds. Perceptual learning of natural sounds induced “sparsening” and decorrelation of the neural response, consequently improving discrimination of these complex sounds. This signature of plasticity in A1 highlights its role in coding natural sounds.

Highlights

  • Learning is accompanied by plastic changes in brain circuits

  • A lick response to the non-target was considered a “False Alarm” (FA) trial, which was negatively reinforced by a mild air puff (60 PSI; 600 ms) followed by a 9-s “timeout.” A “no lick” response to the non-target was considered a correct rejection (CR) and was not rewarded

  • To study perceptual learning in mice, we developed a behavioral platform named the “Educage”

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Summary

Introduction

Learning is accompanied by plastic changes in brain circuits This plasticity is often viewed as substrate for improving computations that sub-serve learning and behavior. A wellstudied example of learning-induced plasticity is following perceptual learning where cortical representations change toward the learned stimuli (Gilbert et al, 2001; Roelfsema and Holtmaat, 2018). Whether such changes improve discrimination has not been causally tested and remains debated, and the mechanisms of change are still largely unknown. Our understanding of the mechanisms underlying auditory cortex plasticity remains rudimentary, let alone for more natural stimuli beyond pure tones

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