Model discovery to link neural activity to behavioral tasks.
Model discovery to link neural activity to behavioral tasks.
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DOI：
10.7554/elife.83289
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发表时间：
2023-06-06
期刊：
影响因子：
7.7
通讯作者：
Haesemeyer M
中科院分区：
文献类型：
--
作者：
Costabile JD;Balakrishnan KA;Schwinn S;Haesemeyer M
大脑并不是针对明确问题的设计解决方案，而是通过作用于随机变化的选择性压力而产生的。因此，尚不清楚实验者选择的模型能否将神经活动与实验条件联系起来。在这里，我们开发了“神经编码模型识别 (MINE)”。MINE 是一个易于使用的框架，使用卷积神经网络 (CNN) 来发现和表征将任务的各个方面与神经活动联系起来的模型。尽管很灵活，CNN 却很难解释。我们使用泰勒分解方法来理解所发现的模型以及它如何将任务特征映射到活动。我们将 MINE 应用于已发布的皮层数据集以及旨在探测斑马鱼体温调节回路的实验。在这里，MINE 使我们能够根据神经元的感受野和计算复杂性来表征神经元，这些特征在大脑中在解剖学上是分离的。我们还发现了一类新的神经元，它整合了我们以前在使用传统聚类和基于回归的方法时无法识别的热感觉和行为信息。
Brains are not engineered solutions to a well-defined problem but arose through selective pressure acting on random variation. It is therefore unclear how well a model chosen by an experimenter can relate neural activity to experimental conditions. Here, we developed ‘model identification of neural encoding (MINE).’ MINE is an accessible framework using convolutional neural networks (CNNs) to discover and characterize a model that relates aspects of tasks to neural activity. Although flexible, CNNs are difficult to interpret. We use Taylor decomposition approaches to understand the discovered model and how it maps task features to activity. We apply MINE to a published cortical dataset as well as experiments designed to probe thermoregulatory circuits in zebrafish. Here, MINE allowed us to characterize neurons according to their receptive field and computational complexity, features that anatomically segregate in the brain. We also identified a new class of neurons that integrate thermosensory and behavioral information that eluded us previously when using traditional clustering and regression-based approaches.