Department of Ophthalmology, Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium.
PLoS Comput Biol. 2021 Mar 2;17(3):e1008714. doi: 10.1371/journal.pcbi.1008714. eCollection 2021 Mar.
In the last two decades rodents have been on the rise as a dominant model for visual neuroscience. This is particularly true for earlier levels of information processing, but a number of studies have suggested that also higher levels of processing such as invariant object recognition occur in rodents. Here we provide a quantitative and comprehensive assessment of this claim by comparing a wide range of rodent behavioral and neural data with convolutional deep neural networks. These networks have been shown to capture hallmark properties of information processing in primates through a succession of convolutional and fully connected layers. We find that performance on rodent object vision tasks can be captured using low to mid-level convolutional layers only, without any convincing evidence for the need of higher layers known to simulate complex object recognition in primates. Our approach also reveals surprising insights on assumptions made before, for example, that the best performing animals would be the ones using the most abstract representations-which we show to likely be incorrect. Our findings suggest a road ahead for further studies aiming at quantifying and establishing the richness of representations underlying information processing in animal models at large.
在过去的二十年中，啮齿动物作为视觉神经科学的主要模型，其地位不断上升。这在信息处理的早期阶段尤其如此，但有许多研究表明，即使是更高层次的处理，如不变物体识别，也会发生在啮齿动物身上。在这里，我们通过将广泛的啮齿动物行为和神经数据与卷积深度神经网络进行比较，对这一说法进行了定量和全面的评估。这些网络已经通过一系列卷积和全连接层被证明可以捕捉灵长类动物信息处理的标志性特征。我们发现，仅使用低到中层的卷积层就可以捕捉啮齿动物的物体视觉任务的性能，而没有任何令人信服的证据表明需要使用在灵长类动物中模拟复杂物体识别的高层。我们的方法还揭示了以前的假设所带来的令人惊讶的见解，例如，表现最好的动物将是使用最抽象表示的动物——我们表明这可能是不正确的。我们的研究结果为进一步的研究提供了一个方向，旨在量化和确定在大型动物模型中信息处理所基于的表示的丰富性。
