Convolutional neural networks trained on object recognition derive some inspiration from the neuroscience of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the hierarchical organization of primates, the visual system of the mouse has flatter hierarchy. Since mice are capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a framework for building a biologically constrained convolutional neural network model of lateral areas of the mouse visual cortex. The structural parameters of the network are derived from experimental measurements, specifically estimates of numbers of neurons in each area and cortical layer, the interareal connectome, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. The code is freely available to support such research.

中文翻译：

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由鼠标神经解剖数据驱动的卷积网络架构

经过对象识别训练的卷积神经网络从灵长类动物视觉系统的神经科学中获得了一些启发，并已被用作在灵长类动物腹侧流中进行的前馈计算的模型。与灵长类动物的层次结构相反，鼠标的视觉系统具有较平坦的层次结构。由于小鼠具有视觉指导的行为能力，因此引发了有关体系结构在神经计算中的作用的疑问。在这项工作中，我们介绍了一个框架，用于构建鼠标视觉皮层外侧区域的生物约束卷积神经网络模型。该网络的结构参数来自实验测量值，特别是每个区域和皮质层，区域间连接体，以及皮质层之间连接的统计信息。该网络旨在支持详细的任务优化的小鼠视觉皮层模型，其中神经种群可与小鼠大脑中特定的相应种群进行比较。该代码可免费获得以支持此类研究。