A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy
A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy
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DOI：
10.1016/j.neuron.2018.03.044
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发表时间：
2018-05-02
期刊：
影响因子：
16.2
通讯作者：
McDermott, Josh H.
中科院分区：
文献类型：
--
作者：
Kell, Alexander J. E.;Yamins, Daniel L. K.;McDermott, Josh H.
听觉神经科学的一个核心目标是建立定量模型，预测皮质对自然声音的反应。由于听觉皮层的完整模型必须解决生态相关的任务，我们优化了用于语音和音乐识别的分层神经网络。表现最好的网络包含独立的音乐和语音通路，遵循早期的共享处理，可能复制人类皮层组织。该网络执行了两项任务以及人类，并表现出类似人类的错误，尽管没有进行优化，这表明网络和人类性能存在共同的限制。该网络预测fMRI体素反应大大优于传统的spectrotemporal滤波器模型在整个听觉皮层。它还提供了一个定量的皮质代表性层次的主要和非主要的反应，最好预测的中间和后期网络层，分别。结果表明，任务优化提供了一套强大的工具，用于模拟感觉系统。
A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems.