CRCNS: Identifying principles of auditory cortical organization with machine learning

人类的听觉系统将传入的声音信息转换成不同的听觉“对象”， 可以被解释、本地化，并与来自其他感官的信息相结合。人类监听者可以 例如，将单声道音乐录音解析为钢琴和吉他，或将语音音频解析为 单词的顺序。我们目前不知道这是如何实现的，也不知道 大脑皮层的声音处理起到了作用，特别是初级听觉皮质以外的区域。 另一方面，我们现在有了另一个更容易调查的系统，从最后一个 十年-在人类表现水平上解决这样的问题：人工神经网络(ANN)。 尽管人工神经网络作为生物物理模型，但在以下方面与生物神经网络非常相似 计算和表示。我们建议比较猕猴的单单位电生理学 带有最先进的人工神经网络的听觉皮质被训练来解决与生态相关的听觉任务。这 组合使我们能够跟踪感知表征中的转换是如何分布的，以及 在大脑中实例化；试验多个ANN体系结构和任务，以测试以下假设 为什么表示采取我们观察到的形式；并反复改进我们的刺激方案和 模特们。我们的目标是(1)评估这些神经网络作为猕猴神经元的编码模型 在听觉辨别任务中记录的听觉皮质；(2)利用神经网络的内部结构来 生成并测试关于非主要组织的地形和等级组织的新假设 听觉皮质；以及(3)演示对非初级神经元的“基于刺激的控制”。 听觉皮层：通过人工神经网络优化刺激，然后在闭环系统中向动物播放 神经生理学实验。

The human auditory system transforms incoming acoustic information into distinct auditory “objects” that can be interpreted, localized, and integrated with information from the other senses. A human listener can resolve, for example, a monophonic musical recording into piano and guitar, or parse speech audio into a sequence of words. We do not currently understand how this is achieved, nor how transformations in sound processing across cortical regions contribute, especially beyond primary auditory cortex. On the other hand, we now have available another, more easily investigated system that—as of this last decade—solves such problems at human performance levels: the artificial neural network (ANN). Although crude as biophysical models, ANNs strongly resemble biological neural networks in terms of computation and representation. We propose to compare single-unit electrophysiology in macaque auditory cortex with state-of-the-art ANNs trained to solve ecologically relevant auditory tasks. This combination allows us to track how transformations in perceptual representations are distributed and instantiated in the brain; to experiment with multiple ANN architectures and tasks to test hypotheses about why the representations take the forms we observe; and to refine iteratively our stimulus protocols and models. Our objectives are (1) to evaluate these ANNs as encoding models for neurons in macaque auditory cortex, recorded during auditory discrimination tasks; (2) to use the internal structure of ANNs to generate and test novel hypotheses about the topographical and hierarchical organization of non-primary auditory cortex; and (3) to demonstrate “stimulus- based control” of neurons throughout nonprimary auditory cortex: optimizing stimuli via the ANN and then playing to the animals in closed-loop neurophysiology experiments.