Interpreting wide-band neural activity using convolutional neural networks.
Interpreting wide-band neural activity using convolutional neural networks.
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DOI：
10.7554/elife.66551
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发表时间：
2021-08-02
期刊：
影响因子：
7.7
通讯作者：
Barry C
中科院分区：
文献类型：
--
作者：
Frey M;Tanni S;Perrodin C;O'Leary A;Nau M;Kelly J;Banino A;Bendor D;Lefort J;Doeller CF;Barry C
钙成像和电生理学等技术的快速发展使神经记录的大小和范围急剧增加。即便如此，对这些数据的解释需要对表示的性质有相当的了解，并且通常依赖于手动操作。解码提供了一种推断这种记录的信息内容的手段，但通常需要高度处理的数据和编码方案的先验知识。在这里，我们开发了一个深度学习框架，能够直接从宽带神经数据中解码感官和行为变量。该网络几乎不需要用户输入，并概括了刺激，行为，大脑区域和记录技术。一旦经过训练，它就可以被分析以确定神经代码中关于给定变量的信息元素。我们验证了这种方法使用电生理和钙成像数据从啮齿动物的听觉皮层和海马以及人类皮层电图（ECoG）数据。我们成功地解码手指运动，听觉刺激和空间行为-包括一个新的表示头部方向-从原始神经活动。
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity.