卷积神经网络模型描述了听觉皮层中局部电路的编码子空间
Jereme C Wingert1,2, Satyabrata Parida2, Sam Norman-Haignere3
1Behavioral and Systems Neuroscience Graduate Program, Oregon Health and Science University, Portland, OR 97239, USA.
bioRxiv : the preprint server for biology
|November 22, 2024
概括
卷积神经网络 (CNN) 更好地预测听力皮层的活动. 一种新的方法可视化了CNN,揭示了神经元如何表示声音特征,并在听觉皮层 (A1) 中形成稀疏网络.
科学领域:
- 神经科学是一个神经科学.
- 计算式听觉神经科学 计算式听觉神经科学
- 在神经科学中的机器学习
背景情况:
- 听觉皮层通过使用非线性组合来处理复杂的光谱-时间声音特征.
- 卷积神经网络 (CNN) 是通过自然声音唤起神经活动的准确编码模型.
- 解释CNN在神经科学中的表现背后的计算机制仍然具有挑战性,因为它们的复杂性.
研究的目的:
- 开发一种可视化CNN在听觉皮层中捕获的调子空间的方法.
- 了解使CNN在预测神经反应方面表现优异的计算特性.
- 为了更好的解释性,将深度学习模型与既定的神经科学概念联系起来.
主要方法:
- 使用高密度微电极阵列记录了来自的初级听觉皮层 (A1) 的单个单元数据.
- 设置一个CNN来预测由大自然声音集唤起的神经活动.
- 测量了CNN的动态光谱-时间受体场 (dSTRF),并使用主要组件分析 (PCA) 来定义一个低维的调子空间.
主要成果:
- 一个来自CNN的dSTRF的PCA的子空间模型,预测神经活动几乎与完整的CNN一样准确.
- 在调子空间的可视化显示了各种非线性反应,包括对比度增强控制和相位不变.
- 当地人群中的神经元构成了调子空间,形成了稀疏的表示;抑制性神经元表现出不同的调模式.
结论:
- 开发的子空间可视化方法为解释神经科学中的深度学习模型提供了一个框架.
- 在听觉皮层中,CNN捕获复杂的,非线性光谱-时间处理,可以有效地在低维子空间中表示.
- 这种方法揭示了神经编码原理的洞察力,例如稀疏编码和A1.1.中的不同神经元类型的功能角色.
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