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深度神经网络解释了听觉皮层中的尖端活动

Bilal Ahmed¹, Joshua D Downer², Brian J Malone^2,3

¹Elmore School of Electrical and Computer Engineering, Purdue University.

bioRxiv : the preprint server for biology

|November 28, 2024

概括

训练在语音上的人工神经网络 (ANN) 能够在精细的时间尺度上预测听力皮层中的神经活动. 这些ANN在解释神经对听觉刺激的反应方面优于传统模型.

科学领域:

神经科学是一个神经科学.
计算神经科学是一种神经科学.
审计处理审计处理

背景情况:

人工神经网络 (ANN) 擅长预测灵长类动物视觉和听觉皮层对静态刺激的神经反应,并且在较慢的时间尺度上.
在听力皮层中预测细粒度尖端活动的ANN的有效性,对于处理语音等动态声音至关重要,仍然在很大程度上未被探索.

研究的目的:

调查在语音音频中训练有素的ANN是否可以预测听力皮层中神经尖端活动在精细的时间尺度 (50毫秒及以下) 中.
为了比较训练有素的ANN的预测能力与传统的光谱-时间-感受场模型和未经训练的网络.

主要方法:

利用了在语音音频数据集上训练的ANN.
从松鼠子的听觉皮层进行了急性多电极记录.
分析了神经尖峰数量,以响应使用不同时间区宽度的语音和子发音.

主要成果:

训练有素的ANN在50毫秒以下的时间尺度上成功预测了听觉皮层中神经元峰值的数量.
与传统的光谱-时间-感受场模型和未训练的网络相比,ANN解释了显著更多可解释的神经变异.
更深层的ANN层显示出非初级神经元的更好预测,表明层特定的处理,尽管观察到显著的神经元到神经元的变化.

结论:

训练有素的ANN是模拟听觉皮层中细粒度神经动态的强大工具,将其预测成功扩展到快速的时间处理.
这些发现突出了ANN在听觉中揭示复杂的神经编码策略的潜力,这些可能会被粗略的分析方法遗漏.

关键词:

人工神经网络的人工神经网络听觉皮层的听觉皮层.自动语音识别自动语音识别灵长类的灵长类动物.

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