深度神经网络解释了听觉皮层的激增活动
Bilal Ahmed1, Joshua D Downer2, Brian J Malone2
1Elmore School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, United States of America.
PLoS computational biology
|August 25, 2025
概括
在语音上训练的人工神经网络 (ANN) 可以预测听觉皮层中的神经活动. 这些ANN比以前的模型更好地解释神经反应.
科学领域:
- 神经科学
- 计算神经科学
- 机器学习
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.
- 对于精细时间尺度的尖端活动来说,ANN的预测能力对于试听至关重要,仍然在很大程度上未被探索.
研究的目的:
- 调查受过语音训练的ANN是否可以预测听力皮层在精细时间尺度上的活动.
- 将训练有素的ANN与传统的光谱时间受体场和未经训练的网络进行比较.
主要方法:
- 使用在语音数据集上训练的ANN.
- 从松鼠的听觉皮层进行了尖的多电极记录.
- 分析了多个单元的尖峰数量,以响应使用不同时间区宽度 (≤50 ms) 的语音和子发音.
主要成果:
- 受过训练的ANN在50毫秒以下的时间尺度上成功预测了听觉皮层中的多个单元峰值.
- 与传统的光谱时间受体场和未训练的网络相比,ANN解释了显著更容易解释的神经变异.
- 较深的ANN层显示出非初级神经元的更好可预测性,尽管具有相当大的神经元特异性变化.
结论:
- 训练有素的语音ANN提供了一个强大的工具, 可以在精细的时间尺度上预测听力皮质活动.
- 这种方法在解释神经变异方面超越了传统方法, 为听觉处理提供了新的见解.
- 这些发现突显了ANN在复杂感官系统中理解神经编码的潜力.
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