双向的生成对抗性表示学习,用于自然刺激合成
Johnny Reilly1, John D Goodwin1, Sihao Lu1
1Department of Bioengineering, Imperial College London, London, United Kingdom.
Journal of neurophysiology
|August 28, 2024
概括
一个新的AI模型,BiWaveGAN,为研究产生现实的动物发声. 这些合成声音有效地刺激听觉神经元,与自然发声的影响相匹配.
科学领域:
- 神经科学是一个神经科学.
- 生物声学是一种生物声学.
- 人工智能的人工智能
背景情况:
- 动物的发声是复杂的信号,对沟通至关重要.
- 分析和合成这些信号是具有挑战性的,因为它们的高维度和人类偏见的潜力.
- 了解听觉神经元是如何表现出伦理相关刺激的,是感官神经科学中的关键.
研究的目的:
- 开发一种方法来产生自然主义的动物发声波形,用于作为刺激.
- 创建一个双向生成对抗网络 (GAN),能够学习语音的潜在表示.
- 为了研究目的,合成新的发音,并将现有的发音插入.
主要方法:
- 开发了一个双向生成对抗网络 (GAN) BiWaveGAN.
- 在鼠标超声波发音 (USV) 上训练BiWaveGAN,以学习隐藏的表示.
- 通过训练模型生成现实的USV波形,并在它们之间进行插入.
主要成果:
- BiWaveGAN成功地生成了鼠标现实的USV波形.
- 合成的发声有效地刺激了小鼠的听觉皮层神经元,与自然的USVs相比.
- 生成的刺激产生了具有与自然发声相当的预测能力的受体场.
结论:
- BiWaveGAN提供了一个强大的工具,用于生成高质量,自然的动物声调.
- 在探测神经元反应方面,合成刺激与自然发声一样有效.
- 这种方法可以应用于各种物种,用于研究听觉处理和分类感知.
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