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.

中文翻译：

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学习听觉皮层中纯音或自然声音的神经相关性。

众所周知，纯音的联合学习会导致听觉皮层（ACx）中的音调图扩展，但这种可塑性所起的作用尚不清楚。我们开发了一个称为“教育”的自动化培训平台，该平台用于对进行/不进行听觉歧视任务的小鼠进行训练，使其达到其感知极限，以区分纯音或自然声音。如先前文献所预期的，小鼠ACx中兴奋性和抑制性小白蛋白（PV +）L2 / 3神经元的尖峰响应揭示了学习诱导的学习频率的过度代表。兴奋性神经元和抑制​​性神经元的协调可塑性支持PV +神经元在电路内稳态维持兴奋性-抑制性平衡方面的作用。使用新颖的计算模型来研究听觉调音曲线，我们表明，学习音调的过度表示并不一定会改善网络对这些音调的辨别性能。在另一组实验中，我们训练了老鼠以区分自然声音。对自然声音的感知学习会引起神经反应的“稀疏”和去相关，因此改善了对这些复杂声音的辨别力。A1的可塑性标志着其在自然声音编码中的作用。因此，改善了对这些复杂声音的辨别力。A1的可塑性标志着其在自然声音编码中的作用。因此，改善了对这些复杂声音的辨别力。A1中这种可塑性的特征突出了其在编码自然声音中的作用。